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Analysis of the role and scope of big data analytics with IoT in health care domain
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Analysis of the role and scope of big data analytics with IoT in health care domain Sushruta Mishra1, Brojo Kishore Mishra2, Hrudaya Kumar Tripathy1, Arijit Dutta1 1
KIIT University, Bhubaneshwar, Odisha, India; 2C. V. Raman College of Engineering, Bhubaneshwar, Odisha, India
Chapter Outline 1. Introduction 2 2. Sources of health care data 2.1 2.2 2.3 2.4 2.5
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Electronic health records (EHR) 2 Clinical text mining 3 Medical imaging data 3 Genomic data 3 Behavioral data 4
3. Tools and data analytics interfaces in medical and health care system 4 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16
Advanced data visualization (ADV) 4 Presto 4 Hive 4 Vertica 4 Key performance indicators (KPI) 5 Online analytics processing (OLAP) 5 Online transaction processing (OLTP) 5 The Hadoop distributed file system (HDFS) 5 Casandra file system (CFS) 5 Map reduce system 5 Complex event processing (CEP) 5 Text mining 6 Cloud computing 6 Mahout 6 JAQL 6 AVRO 6
4. Health care with big data challenges
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4.1 Issues related to policy and fiscal factors 4.2 Issues related to technology 7
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5. IoT defined 7 Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00001-5 Copyright © 2020 Elsevier Inc. All rights reserved.
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2 Chapter 1 6. 7. 8. 9. 10. 11.
IoT for health care 8 Challenges for IoT in health care 9 Evolution of big data in medical IoT 10 Advantages 12 Literature survey 13 Implementation of a real-time big data analytics of IoT-based health care monitoring system 15 11.1 Components and methods 16 11.2 Results and discussion 19
12. Conclusion 22 References 23
1. Introduction Data analytics acts as a major aspect for application in various fields. Data analytics have emerged as a vital tool for scientists due to the heightened presence of heterogeneous and unstructured data around the world. Scalable data analytics techniques are needed; in medical sectors, massive data is regularly aggregated in several organizations. These data sources act as a resource in deriving insights for enhancing care delivery and minimizing waste. The volume and complex nature of such data is a challenge in analyzing and applying in a real-life health care environment.
2. Sources of health care data Datasets gathered in health care domains includes quantitative and qualitative data. Quantitative data is of quantifiable nature and used for comparison purpose. Examples include weight, age, temperature, or any other discrete variables. Qualitative data are nonnumerical in nature which is used to represent health related problems. Some examples include male/female or smoker/non-smoker etc. Data sources in medical field include scientific data and clinical data. Clinical data include data related to clinical surveys or epidemiological based information. Scientific data denotes data related to bench sciences. Data recorded and collected in medical domain are of primary and secondary in nature. Primary data refers to the individual person or a group to collect and analyze the data. This collected data may be used for research queries. Secondary data is dependent on the existing data which are already available and is utilized for other purpose. These data are used to answer research-based questions. Fig. 1.1 highlights the health care sources of data samples.
2.1 Electronic health records (EHR) This is an important source of data in medical field. Electronic health records (EHR) refers to the digital records of patients. Here the data can be accessed from anywhere and
Analysis of the role and scope of big data analytics with IoT in health care domain 3 Medical Imaging Data
EHR data
Clinical Text Mining
Sources of Health care Data
Genomic Data
Behavioral Data
Figure 1.1 Health care data sources.
whenever required. It may be structured or unstructured. In structured data, all records are properly captured and categorized in a database. But unstructured data records are vague and inconsistent which are presented in the form of static pages of health information. Examples include PDF files, emails, and digital images, audio, or video information. Ultimately these data are transformed into structured records.
2.2 Clinical text mining Health care records can be structured, unstructured, or can have text related information. Here text mining can be used to extract useful and sound information from huge raw data. Few text mining methods involve categorization and sentiment analysis. It is used for optimum targeting of drugs, precise disease diagnosis and efficient patient treatment. Natural language processing may be used in health care text mining.
2.3 Medical imaging data CT scans and X-rays belongs to unstructured data type. Picture Archival & Communication Systems is a system used to store and retrieve clinical imaging data records. In clinical retrieval process, images are deposited in repository of biomedical image data. These image-based data takes huge memory and is complicated in processing.
2.4 Genomic data These data handles DNA aspects in structural and sequential arrangement of various functionalities of genes. Specific software is required to store and process these data. A repository called genomic database comprises human genomes and association rules related to genomes. This repository determines the identical genetic symptoms influencing health and its associated diseases.
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2.5 Behavioral data The source of behavioral data lies in mobility-based sensor data associated with social network. There exist some social networks which keep track of diseases and their symptoms. Accordingly, they offer better treatment process based on the symptoms involved. Similarly, sensors can be deployed to gather and aggregate disease related data from patients in a health care institutions.
3. Tools and data analytics interfaces in medical and health care system There exist various tools and applications which are used to determine the progress in clinical data analysis. Some of the widely popular tools used are presented below.
3.1 Advanced data visualization (ADV) ADV is useful to deal with several types of data. It changes from line chart to standard bars. It is quite easy to use. It offers wide support to analysts in data exploration. It produces very optimum results and used to extract medical hidden patterns in health care data.
3.2 Presto Presto is a distributed SQL Query engine used in analyzing massive quantity of clinical data. It is applied in a large-scale analysis where data analysis can be done without significant delay.
3.3 Hive It is also applied to deal with large scale data records. It is not so fast like Presto tool. In fact it performs all Excel sheet tasks effectively. Many industries prefer Hive for medical records storage and retrieval.
3.4 Vertica This tool is identical to Presto and is utilized in processing huge amount of clinical based data which may be further used for data analytics. It is cost effective and its architecture is simple. It is very scalable in nature. It is advantageous in reducing operational costs, speeding up health care reports and documentation thereby helps in analyzing health patterns of patients.
Analysis of the role and scope of big data analytics with IoT in health care domain 5
3.5 Key performance indicators (KPI) This represents a procedure which makes application of electronic health care records in determining inventions and practices of human beings. Patients who are more vulnerable to hospital environment may be subjected to KPI tool to get better results.
3.6 Online analytics processing (OLAP) Here the data is organized in multidimensional patterns which perform statistical computation at a great speed. It amplifies data integrity constraints and establishes better quality control. It keeps track of health care records and helps in disease diagnosis.
3.7 Online transaction processing (OLTP) OLTP and OLAP are interrelated to each other. This tool is useful in processing registration of patients, analyze various operations of patients and result review analysis.
3.8 The Hadoop distributed file system (HDFS) The performance of clinical data analytics is improved by the use of HDFS which partitions huge data sets into relatively smaller ones. These smaller data samples are distributed across entire system. It removes redundancy of data. It acts as a diagnosis assisting tool and is used to monitor and detect fraud elements and patients symptoms.
3.9 Casandra file system (CFS) It is very much identical to HDFS. This file system is designed to handle analytical operations and is fault-tolerant.
3.10 Map reduce system This system deals with massive amount of data. It partitions the chore into subchores and aggregates its output. It efficiently integrates various operational computations into the system. It tracks every server where the chore is being done. The main benefit lies in its higher degree of parallel tasks.
3.11 Complex event processing (CEP) It is a recent addition in medical sector which helps in monitoring different phases of patient. Complicated event processing is interlinked in real-time analysis.
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3.12 Text mining In medical field text mining systems may act as an advantage in examining medical records from medical centers. It can be useful in devising treatment plans which can develop many protocols. Further treatment of patients can be undertaken relating to such developed guidelines.
3.13 Cloud computing Cloud computing technology offers higher flexibility in medical field as far as dealing with adaptive variations and health care updates are concerned. It is an addition to clinical sector by reducing the medical costs, enhancing the productivity an optimizing data analysis task.
3.14 Mahout Mahout is an apache-based project which is intended in developing applications to improve clinical data analytics on Hadoop systems.
3.15 JAQL JAQL is a procedure-oriented query language useful in processing massive amount of data. Parallel processing of data is feasible by converting higher level queries into lower level ones. It is well suited to work with Map reduce functions.
3.16 AVRO AVRO is effective in encoding and serialization of data. It enhances data semantics by specifying types of data samples used, semantics and its schema.
4. Health care with big data challenges The challenges can be categorized into two types:
4.1 Issues related to policy and fiscal factors In the age of money for service scenario, the medical experts can get paid only when they have a face to face interaction with their patients. It acts as a bottleneck to promote new technologies that encourage interaction without physical presence of patients. Moreover, as we go further away from direct interaction-based models, where there are more financial risks are involved there is more scope of using recent advanced technologies where
Analysis of the role and scope of big data analytics with IoT in health care domain 7 unnecessary face to face interactions may be avoided. In such cases, face to face interactions with patients are quite expensive while use of advance technologies impacts a positive influence in health outcomes of people.
4.2 Issues related to technology One of the largest technical obstacle to achieve this mission is the status of medical data. Developed by EHR systems, medical based data records are highly segmented into organization-based silos. Maximum effort is given to deal with this exchange of individual data records in between silos with the use of standard code sets and message structure. But it fails to solve the data fragmentation issue. Recently people in medical arena are visualizing the future generation of medical field lies in data aggregation and not just sharing copies of patient records. The data can be made relevant and useful only when data can be gathered from heterogeneous sources and further normalizing the gathered data and resolving the information with unique identifiers of patients. There are two main benefits of aggregated data. •
•
It resolves the interoperability issue. Organizations are no more required develop data bridges and convert the data between proprietary systems. They just need to connect data sources to a common API module. This data aggregation forms the basis of effective artificial intelligence technology. It provides adequate flexibility thereby allowing artificial intelligence and machine learning to operate efficiently in real-time manner.
5. IoT defined IoT refers to a computational notion to describe the concept of daily physical objects which are connected to internet such that they are able to identify and distinguish themselves from the rest. This methodology is acutely associated with RFID as the transmission technique. Besides this, it involves sensor and wireless technologies or QR codes. The significance of IoT lies in the fact that the digital representation of object becomes more visible that the object itself. The object is no more interrelated to its user but also is related to its neighboring objects. Some crucial focus areas where IoT analytics can be successfully applied are: • • • • • •
Forecasting the agriculture production/manufacture Machine learning algorithms Failure prediction Predictive maintenance Supply and chain Frequent pattern mining.
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6. IoT for health care The main aim of technology in medical field is to connect health care experts with their patients through smart devices. This helps patients to be more aware of their issues and thus the diagnosis becomes effective. It empowers consumer with less inefficiencies and assists doctor in precise decision making. IoT in health domain serves two main purposes which are: • •
Enhanced management of disease providing better patient experience. Decreased medical costs to make it more affordable for a wider demographic population.
As shown in Fig. 1.2, a survey was conducted by Grand View Research where it is estimated that by the year 2022, IoT in health filed which covers the domain of medical equipment, services, and software is presumed to jump a whopping $300B market expansion. Central agency schemes are also likely to impact and encourage this value for customized smart health care. Fig. 1.3 highlights the architectural view needed in clinical IoT systems. It consists of three prime components, which include the device layer, which has the body area sensor network embedded into it; Internet-connected smart local access network, which is called a Fog layer; and a Cloud layer for cloud and big data service support. Several applications and firms provide services to various stakeholders within the system by the use of this model. Sensors attached to users are responsible to generate data which is made readily available to medical experts, family members and authorized firms enabling them to verify and validate the issues and diagnosis process at anytime from anywhere as well as
Figure 1.2 Coverage analysis of IoT in health care services.
Analysis of the role and scope of big data analytics with IoT in health care domain 9
Figure 1.3 Architectural elements of health care IoT systems [1].
assisting health care experts in intelligent decision making. In this new age of information, knowledge extracted from raw data is the need of the hour. This is the age of customers where their associations with health care world are a priority. At this juncture, apps and devices will be applied to develop a health-aware environment. Some of these devices include: • • • • • • • • • •
OpenAPS: closed-loop insulin delivery Continuous glucose monitoring (CGM) system Activity trackers during cancer treatment Connected inhalers Ingestible sensors Connected contact lenses Depression-fighting Apple Watch app Coagulation testing Arthritis: Apple’s ResearchKit Parkinson’s Project Blue Sky
7. Challenges for IoT in health care Prime objective of reputed IoT firms is to provide simple and powerful implementations to services of IoT and data handling facility. It helps designers to compose data analytics applications, visualization frameworks and health care IoT apps. Some of the critical capabilities that IoT organizations must be enabled are: •
Simple connectivity: An ideal IoT firm must be competent enough to provide ease of connection to devices thereby facilitating device management functionalities.
10 Chapter 1 • •
•
•
Easy device management: It enables enhanced availability of different assets and resources which lead to improved throughput and reduction in maintenance costs. Information ingestion: Intelligent transformation and storage of data is a vital factor In IoT. Information is ingested from distinct sources of data and then relevant information is extracted with the use of data analytics. Informative analytics: Proper analysis of raw information is important for optimal decision making and smooth operations. It is used in real-time analytics and monitoring present conditions to respond accordingly. Moreover an intuitive dashboard makes it more simple and effective to understand. Reduced risk: Act on warnings and isolate activities collected somewhere in the organizations from a unit console.
8. Evolution of big data in medical IoT The health care industry is the combination of different sectors. The sectors has the inclusion of medicines, precautionary with the statistical working of the properties and the socialization of care. Health care industries have been included with different nursing home, medical trials, outsourcing, health coverage, telemedicine and other charitable organization. These days the health care industry has been seen from a business point of view. The business provides good profits and other services for the people. Along with this there has been an inclusion of information and communications technology. The ICT cell has been able to provide the health care industry with the provision of different roles for the improvement of health care industry. The system would be able to help the existing system in the exclusion of medical errors. Information related to the system is generated from different sources. Sources for the data includes clinical trials, medicine, exercise, variable symptoms, prescription, laboratory report, insurance data and various other information related to the patient and doctors. The large amount data in the health care industry is growing in an exponential form with current data size in the order of petabytes. This immense growth of data has given rise to various problems related to storage, transfer, and computational analysis. This form of data can be analyzed and processed with the help of traditional relational database system. Moreover, traditional database system can only process structured data. Whereas the data stored in the form of big data is unstructured. With the invention of new and efficient mechanism for the storage and the accessing of the information the ICT would be able to help serve the society in a better storage. The process of implementing of ICT in the health care industry in termed as eHealth. Thus the implementation in the health care industry would help in the processing of data and consecutive analysis and the improvement of the decision-making process for the collection of better treatment solutions for the symptoms for the diseases. One of the top characteristics of the use of health care industry is the richness of data. With the recent development of the diagnostics and the treatment processes, the health care industry has
Analysis of the role and scope of big data analytics with IoT in health care domain 11 been used to quickly evolve the sector in the previous couple of decades. Several sources are used for generation of big data and the sources are considered as following: •
• •
•
Web and social media: Captured data from Facebook, LinkedIn, Twitter, blogs, and shared stories on social media. The data captured from health planning webpages, smartphone applications and other sources as well. Machine-to-machine device generated data: Remote sensor data, meter readings and other device readings are recorded as machine-to-machine device generated data. Biometric data and demographic data: Biometric data such as capturing data from retinal scans, X-ray images, fingerprints, handwriting, blood pressure and other similar type of data can be recorded as demographic sources as well. Human-generated data: Unstructured and semistructured data can be captured from manual data entering and some data examples are considered as EMRs, doctor’s Prescription, and other paper documentations.
Big data concept represents a technology that stores and analyses datasets of massive size such as petabytes, terabytes, exabyte, and zettabytes. Such measure of data that is generally out of human expertise to store data in manual effort; capturing and analyzing the data can include significant value for business and decision-making process. Hence, organizations today are making the most from Big Data technologies to store process and analyze the data generated. Three important characteristics or the three Vs of big data are volume, velocity, and variety (Fig. 1.4). The data can be structured, unstructured, or semistructured. Structured data has a predefined arrangement of data; examples include
Figure 1.4 Three Vs (velocity, volume, and variety).
12 Chapter 1 Healthcare Services Payors & Providers
101010101010 001000100010 001100110011
Massive Data Flows: Clinical Claims, Lab, Rx, Demographics, Benefits, DME, EHR, Clinical, Patient-Reported, and Supplemental Data Sources
Big Data Analytics Processing
Pharma Integration & Interoperability Impact Enablement
Devices
Diagnostics The MORE2 Registry© Database: Proprietary Design, Research and Functionality
Patients & Consumers
Figure 1.5 Big data analytics in health care: key components.
address data books, product information catalogs, and banking data. Unstructured data do not have predefined arrangement; examples include audio files, video files, text records, web sheets, computer programs, and social websites. Semistructured data is neither fully structured nor fully unstructured, and 80% of data is unstructured. Big data is interrelated with analytics and analytics is a familiar concept. Regression methods, simulation, and machine learning algorithms are the analytic techniques that were in use from past several years. The analytics carried out using the above-mentioned techniques for the unstructured data like email, computer programs and documents is understandable. However, the new thing is, as said above the advanced sources in computer technology and software’s like social media sites such as Facebook, Twitter, blogs, and many other business opportunities are generating enormous amount of data every minute. This has given rise to move toward advanced technology called Big Data analytics that supports many tools and technologies for the data analytics. Apart from just analyzing, the huge data there are many other benefits from big data technologies. Fig. 1.5 illustrates the key components of big data analytics in health care.
9. Advantages Here various benefits associated with big data in medical field are discussed. •
Reducing medical costs to achieve financial benefit: Cost of medical treatment can be significantly reduced with big data analytics. Also efficient data analysis provides information to physicians to determine populations at risk for ill-health. Hence proactive actions may be taken at prior. Big data can be applied accurately to analyze the scenarios where education and preventive actions are needed to get more healthy
Analysis of the role and scope of big data analytics with IoT in health care domain 13
•
•
•
•
•
•
populations at less cost. Treatment becomes more evidence related on applying big data analytics. Personalized medicine: Through genetic blue prints, medical experts can accurately predict the diseases and its risk factors. Big data further helps in personalization of medical drugs by determining proper treatments of patients. Patient treatment at early phase can reduce the risk of chronic disease factors. Strengthening preventive care: Prevention mechanism is far better than cure. With this rule, applying big data it is simpler to capture process and analyze symptoms of patients to offer a preventive care in an optimal manner. Wearable medical technologies: With the advance in technology, medical experts make better use of wearable devices thereby enhancing the quality of care and providing patients with more accessibility. Clinical trend analysis: By the usage of various analytical techniques which include machine learning and text mining, medical trend analysis and management of patients data become simpler with big data analytics. Detection and tracking patients: Various big data analytics methods like statistical clustering can be used to cluster group of patients suffering from different diseases. This technique applies available data samples. Also, patients can be tracked precisely to identify the regular patterns for treatment of disease. Analyzing efficacy of drugs: Studying drug efficacy may be done with electronic health record data. A comparative analysis of EMR data and hospital medical records are done and it is observed that cost of trials at random is higher than that of readily available HER based data records to analyze treatment procedures.
10. Literature survey Pagan et al. [2] in their work studied a power-aware huge deployment of a body area network to predict migraine activities of patients across Europe. It aimed to address issues in data acquisition and analysis in clinical sector. Adame et al. [3] developed an IoT monitoring model that combines both sensor network and RFID technology. It helps in tracking the location of medical resources and assets. It also keeps the patients informed about their body temperature, heart rate and movement. It is practically implemented in a hospital environment using back end servers with qualitative feedback analysis. A hybrid technique to sense and monitor locomotive movements by determining variations in wireless signal strength is proposed by Ammae et al. [4]. With the change in signal strength, it allows an unobtrusive method of determining the quality of sleep using maximum likelihood linear regression model. It was evaluated with the usage of 60 iterations of real-time information gathered from 6individuals and the result obtained was very positive and efficient. Woo et al. [5] focused on fault-tolerant medical data services by developing a fault-tolerant and reliable IoT system model. Here the gateways are
14 Chapter 1 interlinked to develop a daisy chain of reliability where the replica of the predecessor gateway can be stored in the daisy chain. It enabled the user to simultaneously recover from a two gateway faults. Rahmani et al. [6] presented a smart e-health care gateway at the network edge in an architectural configuration of a fog computing. Farahani et al. [1] in his article discussed regarding the challenges and scope of IoT in the health care field. A system model was proposed that migrated from hospital-oriented centers to people oriented centers with the help of IoT infrastructure. In Refs. [7,8], an IoT framework for elderly persons was proposed and implemented by observing several physiological features. An IoT-based intelligent wallet system was developed in Ref. [9] which is associated with every individual in order to store signals and wallet shares. A wireless sensor network based IoT model was presented in Ref. [10] that reduced the power consumption at the sensor nodes which enhanced the network lifetime [11]. discusses the impact of IoT concept with respect to the overall geographical range. Developed countries are rapidly progressing in IoT field but it is the less developed regions where IoT technology is crucial for the overall development of humanity. Authors in Ref. [12] developed a heart monitoring gadget with the help of remote sensors and advanced smart phone technology. It sends an alarming signal to the medical experts and family members in case any discrepancies are observed in the patient. The security and privacy concerns in IoT can be properly addressed by the usage of body sensor network technology which is illustrated in Ref. [13]. Authors in Ref. [14] present an elaborate analysis on the collaboration of cloud and IoT together. These two technologies can work together to offer efficient health care monitoring system model where huge amount of data can be processed and results can be obtained in real time more accurately [15]. developed and analyzed a pervasive surveillance system with mesh topology which is used as data compression processing system for patients in hospitals. Data are aggregated and stored in cloud server. Real-time data in the form of images and video are generated and analyzed. Any variation in data recorded is immediately monitored and appropriate actions are taken on patients [16]. discusses the significance of data quality and reliability as two important factors in health careebased IoT applications. The clinical analytics with the use of an effective anomaly identification model was proposed to detect any early and prompt inconsistencies of dominant diseases. Authors in Ref. [17] proposed a health care system for elderly patients. It employed a central unit model which facilitated in decision making which could detect any critical anomalies in elderly people based on the data generated by the sensor units. At the end of analysis in emergency scenario, the system model transmits an alert signal to an emergency control unit. Sushruta Mishra et al. [18] proposes a machine learning-system model based on biologically inspired computation for primary tumor classification. Genetic algorithm was the attribute optimization technique used in the study. Authors in Ref. [19] presented a social network analysis framework for big data analysis on telecommunication domain. similarly various research works are being carried out in relation to big data analytics and IoT filed with respect to medical data analysis.
Analysis of the role and scope of big data analytics with IoT in health care domain 15
11. Implementation of a real-time big data analytics of IoT-based health care monitoring system Complicated tools integrated with IoT are fruitful for the medical experts in monitoring huge health care related data records so as to keep track of their patient’s health condition. According to the signals transmitted by the IoT system, health status of patients is continuously tracked and in case of any abnormalities, alert signals are sent to the physicians. For example, if the glucose level of a patient drops, the system will transmit an alert message to the doctor who can interact with his patient and take appropriate actions. This data is aggregated from patients and deployed in cloud. The cloud environment makes it feasible to gather and aggregate data rapidly and more accurately. The proposed system model integrates various entities like medical amenities, physicians, vital medical services, patients and other technologies to extract the relevant value form the gathered medical data records. Salient features of the proposed system model include the following: • • • • •
Sensors are used to collect health data of patients which are further used for processing and communication purpose. Intel Galileo Gen2 which acts as medical IoT agent are utilized in analyzing sensor related data and used for storage in cloud for analysis. Data analytics of medical related records with the help of the map reduce technique in cloud. Graphical user interfaces controlled by medical monitors for smooth progress. Smart phones embedded with GPRS and GSM connections.
Big data analytics for health care based IoT architecture is represented in Fig. 1.6. The health proxy played by the IoT agent is coupled with the heart rate sensor, blood pressure sensor and humidity sensor. The medical metrics sensed by the system model are employed and integrated within cloud. The cloud concept is used here to analyze such huge medical data generated from various patients across distinct medical organizations. Historical data analysis of medical information for patients is feasible with the stored data in cloud. The physician’s smart phone is used to connect directly to the IoT agent through GPIO pins and collect health careebased data of patients. Appropriate medical help may be provided if any critical medical condition is identified in patients. The proposed health care monitoring model with IoT can also be further integrated with smart phone interfaced with it. The system model shown in the figure offers connectivity to cloud thus delivering end-to-end customer value. Sensor data analytics are executed inside the cloud using HDFS file system and MapReduce method. It is used for storing and processing health care related sensor information.
16 Chapter 1 GALLILEO GEN 2
IOT Agent
IOT Analytics HTTP
GPRS/GSM
Figure 1.6 Proposed IoT architecture.
11.1 Components and methods The developed clinical monitoring model used various types of sensors such as finger moisture sensors, blood pressure sensors and heart beat rate sensors. These sensors collect and aggregate data from patients. This health-monitoring model uses various physiological signals generated from patients. Intel Galileo which acts as an IoT agent is an IBM registered device is selected for implementation purpose. Sensor data values are recorded with the use of Arduino programming. With the help of Intel Galileo Gen2, interfacing with outside world is also feasible. Fig. 1.7 illustrates a heart beat sensor while Fig. 1.8 shows moisture sensor implemented in our developed work. A PCB antenna designed with an industrial based standard interface. ATWIN Quad-band GPRS/GSM shield acts as a wireless module base, which is of very high quality performance based on UCL2 interface. It is a small dimension-based SMT package with less power consumption with a double-band package. It has provision of SMS, voice, data, and fax applications for medical experts. It is shown in Fig. 1.9. • • • •
IoT Proxy’s D0 is connected to Rx of hardware serial port IoT Proxy’s D1 is connected to Tx of hardware serial port IoT Proxy’s 5V are connected to 5V of hardware serial port IoT Proxy’s GND is connected to GND of hardware serial port
Analysis of the role and scope of big data analytics with IoT in health care domain 17
Figure 1.7 IEEE 802.15.4 sender and receiver with heart beat sensor.
Figure 1.8 Intel Galileo Gen2 with moisture sensor.
An unlocked mini-SIM (size of 2 FF) card is associated within it. The IoT Proxy lies in the shield. No extra wiring is necessary. The Serial port select jumpers to the hardware serial position are set as follows: • •
Set J1 to connect Rx to MTx Set J2 to connect Tx to MRx
Fig. 1.10 shows the SIM card inserted into Intel Galileo. The components required for the Intel IoT Developer Kit used in this paper needs to connect the IoT proxy (Intel Galileo Board) over Wi-Fi connection. Through available miniPCIe slot IoT agent provides cloud based connectivity with sensors supporting Wi-Fi service which leads to development of IoT health-monitoring model.The IoT agent is able to abstract the complexities of the
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Figure 1.9 SMT package inserted into Intel Galileo with PCB antenna.
Figure 1.10 SIM card integrated to Galileo Gen2.
connectivity to the cloud. This process helps in the focusing of the application development according to the proposed solution including the health care sensors. The agents’ works in the formatting of data and the security protocol for registering sensors in cloud. With provision of end-to-end service of IoT solution from Intel, it has been implemented in the proposed system. The proposed system would have quick management, connectivity, and protection of the sensors. Physiological metrics like heart beat levels are gathered from sensors and are used for storage on a web cloud server for processing. “Thingspeak” cloud is used in this implementation work. The readings of finger moisture level are recorded at regular intervals and are shown in Fig. 1.11. The medical sensor data records generated in
Analysis of the role and scope of big data analytics with IoT in health care domain 19
Figure 1.11 Deployment of finger moisture in Thingspeak cloud.
Thingspeak cloud are mapped onto Hadoop Distributed File System (HDFS). HDFS acts as the storage for Hadoop cluster. The medical data is split into small divisions and are made available across several servers in cloud. Map reduce programming is used to perform the computational distribution of sensor data. Data is gathered by distributing various subtasks in each server. In the cloud environment, all the server nodes are tracked with the use of map reduce. The map reduce technique with its classes and methods split the large data file into relatively smaller divisions which are further mapped in the cloud. These smaller data partitions are parallel executed to determine the final output. As a result the execution time is reduced. Finally the result of these sensors of map reduce is published to all mobile phones with medical experts after appropriate authentication.
11.2 Results and discussion The patients act upon the heart beat arte sensors and the IoT device records the data. This data is implemented with Thingspeak cloud. This procedure is highlighted in Fig. 1.6. Also the recordings of moisture sensors are displayed. A command tool called Hive is used to retrieve the data recordings of body sensor. Fig. 1.12 depicts the data analytics gathered from the body pressure with Hive query command. It is clearly seen that the response time period of the proposed health care system is very prompt as the completion of the query using Hive command requires only 1.05 s after the Map Reduce processing is over in the cloud, the aggregated sensor information is transmitted to the health care
20 Chapter 1 hive> CREATE TABLE xml7(Level map, Pressure map) >
ROW FORMAT SERDE
'com.ibm.spss.hive.serde2.xml.XmlSerDe' >
WITH SERDEPR0PERTIE5 (
>
"column.xpath.Level"="/Units/Level",
>
"column.xpath.Pressure"="/Unlts/Pressure"
>
)
>
STORED AS
>
INPUTFORMAT 'com.ibm.spss.hive.serde2.xml.XmlInputFormat'
>
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveOutputFormat' >
TBLPROPERTIES (
>
"xmlinput.start"="",
>
Figure 12. Body pressure with Hive query command "xmlinput.endn=" "
>
);
OK Time taken: 1.056 seconds
Figure 1.12 Body pressure with Hive query command.
monitoring system which is highlighted in Fig. 1.13. The smart phone device may be connected to the interface directly. Apart from this, the IoT agent can also communicate with the smart phone by the usage of SMT package and SIM card of the device. This condition is illustrated in Fig. 1.5. The alerting model is developed which is based on the optimal threshold computation which is seen in Fig. 1.14. Once arrhythmia is diagnosed in patient, the alert system immediately sends an alert signal. The model reads the physiological metrics which provides the alert signals after the threshold value is compared. A self-adaptive alert system has been included in the proposed health care system which generates notifies the medical experts about their patients in case any emergency arises. The threshold data values concerning the heart beat rate alert signals are illustrated in Table 1.1.
Analysis of the role and scope of big data analytics with IoT in health care domain 21
Figure 1.13 Health-monitoring Interface.
If HR > 100
No
If HR < 60
No
HR is normal
Yes
Yes
Tachycardia
Bradycardia
Arrhythmia detected
Figure 1.14 Flow chart for alert.
Table 1.2 depicts the data gathered from the alert system model implemented in the proposed health care system. A comparative analysis was carried out and it was determined that the heart beat monitoring has been enhanced with the proposed model. It is measured that the average time taken to transfer the alert signal between the sender and the receiver in presented IoT model is found to be 35 s and this time is within 3G network recorded in Table 1.2. As per big data analytics requirements, the overhead is performed on the heartbeat rate, and thereafter the alert signal is raised.
22 Chapter 1 Table 1.1: Threshold data of heart beat rate alert signals. Sinus rhythm type
Threshold data of heart beat rate
Normal Tachycardia Bradycardia Sinus rhythm type Normal Tachycardia Bradycardia
60 < HR < 100 (beats/minute) HR > 100 (beats/minute) HR < 60 (beats/minute) Threshold value of heart rate 60 < HR < 100 (beats/minute) HR > 100 (beats/minute) HR < 60 (beats/minute)
Table 1.2: Average data transmission time.
Alert for
Mean time in send and receive alert in Wi-Fi (H:M:S)
Tachycardia Bradycardia
00:00:29 00:00:30
Mean time in send and receive alert in 3G network (H:M:S) 00:00:58 00:00:59
Mean time in send and receive alert in proposed IoT system (H:M:S) 00:00:35 00:00:38
12. Conclusion More new technologies are rapidly becoming useful in the health care sector, including devices and models that regularly monitor health parameters and other devices that keep track of real-time medical information. Due to increases in Internet speed and the wide availability of smart phones, patients as well as doctors are using mobile-based applications to regulate their health requirements. Integration of big data analytics with IoT technology plays a vital role in this health care domain. In this chapter we have drawn a detail analysis and discussion about the role and scope of big data analytics and IoT technology in medical field. Later a case study illustrating an IoT-based medical monitoring system model with Big data analytics is presented and its implementation is also highlighted. Here huge quantity of heartbeat data records of patients are gathered and respective medical expert is required to segregate the data according to patient. Intel Galileo Gen2 is the IoT-based agent used in the above implementation to integrate clinical data of concerned patients with Thingspeak cloud. Hadoop framework is applied to process this massive data of patients over cloud. It was observed that the response time was much less without significant delay. Also, it can be deployed in real-time environment for health care monitoring of patients. Clinical metrics coordination is done with the usage of GPRS/GSM connection abilities of Intel Galileo Gen2 through alert signals. Physicians can take benefits from this developed health care system providing appropriate information to appropriate patient at appropriate time. Hence, effective and timely diagnosis can be provided to patients with minimum response time.
Analysis of the role and scope of big data analytics with IoT in health care domain 23
References [1] B. Farahani, F. Firouzi, V. Chang, M. Badaroglu, N. Constant, K. Mankodiya, Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare, Future Generation Computer Systems 11 (2017). [2] J. Pagan, M. Zapater, J.L. Ayala, Power transmission and workload balancing policies in eHealth mobile cloud computing scenarios, Future Generation Computer Systems 3 (2017). [3] T. Adame, A. Bel, A. Carreras, J. Meli-Segu, M. Oliver, R. Pousa, Cuidats: an RFID-WSN hybrid monitoring system for smart healthcare environments, Future Generation Computer Systems 7 (2017). [4] O. Ammae, J. Korpela, T. Maekawa, Unobtrusive detection of body movements during sleep using Wi-Fi received signal strength with model adaptation technique, Future Generation Computer Systems 16 (2017). [5] M.W. Woo, J. Lee, K. Park, A reliable IoT system for personal healthcare devices, Future Generation Computer Systems 15 (2017). [6] A.M. Rahmani, T.N. Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, P. Liljeberg, Exploiting smart ehealth gateways at the edge of healthcare internet-of-things: a fog computing approach, Future Generation Computer Systems 5 (2017). [7] S.M. Riazul Islam, D. Kwak, M.D. Humaun Kabir, M. Hossain, K.-S. Kwak, The internet of things for health care: a comprehensive survey 3 (2015) 678e708. [8] A. Khanna, P. Misra, The Internet of Things for Medical Devices Prospects, Challenges and the Way Forward, 2017. [9] L. Yu, Y. Lu, X.J. Zhu, Smart Hospital Based on Internet of Things, 2012. [10] R. Singh, A proposal for mobile E-care health service system using IOT for Indian scenario, Journal of Network Communications and Emerging Technologies (JNCET) 6 (2016). [11] A. Mathew, S.A. FarhaAmreen, H.N. Pooja, V. Aakriti, Smart disease surveillance based on Internet of Things (IoT), International Journal of Advanced Research in Computer and Communication Engineering 4 (5) (May 2015). [12] S.C. Padwal, S.V. Kurde, LongTerm environment monitoring for IOT applications using wireless sensor network, International Journal of Engineering Technology, Management and Applied Sciences 4 (2016). [13] P. Gope, T. Hwang, BSN-care: a secure IoT e based modern healthcare system using body sensor network, IEEE Sensors Journal 16 (5) (March 2016). [14] Y.-P.H. Haobijam Basanta, T.-T. Lee, Intuitive IoT e based H2U healthcare system for elderly people, in: IEEE 13th International Conference on Networking, Sensing and Control Mexico, April 2016. [15] A.P. Plageras, K.E. Psannis, Y. Ishibashi, IoT e based surveillance system for ubiquitous healthcare, in: IEEE 11th International Conference, Greece, 2016. [16] A. Ukeil, S. Bandyoapdhyay, C. Puri, IoT healthcare analytics: the importance of anomaly detection, in: IEEE 30th International Conference on Advanced Information Networking and Applications, India, 2016. [17] P. Gupta, D. Agrawal, J. Chhabra, IoT based smart HealthCare kit, in: IEEE International Conference on Computational Techniques in Information and Communication Technologies, India, 2016. [18] S. Mishra, H. Kumar Tripathy, B.K. Mishra, Implementation of biologically motivated optimisation approach for tumour categorisation, International Journal of Computer Aided Engineering and Technology 10 (3) (2018). [19] S. Mishra, H. Kumar Tripathy, B.K. Mishra, M. Mishra, B. Panda, Use of social network analysis in telecommunication domain, Modern Technologies for Big Data Classification and Clustering (2017) 152e178.
Analysis of the role and scope of big data analytics with IoT in health care domain Sushruta Mishra1, Brojo Kishore Mishra2, Hrudaya Kumar Tripathy1, Arijit Dutta1 1
KIIT University, Bhubaneshwar, Odisha, India; 2C. V. Raman College of Engineering, Bhubaneshwar, Odisha, India
Chapter Outline 1. Introduction 2 2. Sources of health care data 2.1 2.2 2.3 2.4 2.5
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Electronic health records (EHR) 2 Clinical text mining 3 Medical imaging data 3 Genomic data 3 Behavioral data 4
3. Tools and data analytics interfaces in medical and health care system 4 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16
Advanced data visualization (ADV) 4 Presto 4 Hive 4 Vertica 4 Key performance indicators (KPI) 5 Online analytics processing (OLAP) 5 Online transaction processing (OLTP) 5 The Hadoop distributed file system (HDFS) 5 Casandra file system (CFS) 5 Map reduce system 5 Complex event processing (CEP) 5 Text mining 6 Cloud computing 6 Mahout 6 JAQL 6 AVRO 6
4. Health care with big data challenges
6
4.1 Issues related to policy and fiscal factors 4.2 Issues related to technology 7
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5. IoT defined 7 Handbook of Data Science Approaches for Biomedical Engineering. https://doi.org/10.1016/B978-0-12-818318-2.00001-5 Copyright © 2020 Elsevier Inc. All rights reserved.
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2 Chapter 1 6. 7. 8. 9. 10. 11.
IoT for health care 8 Challenges for IoT in health care 9 Evolution of big data in medical IoT 10 Advantages 12 Literature survey 13 Implementation of a real-time big data analytics of IoT-based health care monitoring system 15 11.1 Components and methods 16 11.2 Results and discussion 19
12. Conclusion 22 References 23
1. Introduction Data analytics acts as a major aspect for application in various fields. Data analytics have emerged as a vital tool for scientists due to the heightened presence of heterogeneous and unstructured data around the world. Scalable data analytics techniques are needed; in medical sectors, massive data is regularly aggregated in several organizations. These data sources act as a resource in deriving insights for enhancing care delivery and minimizing waste. The volume and complex nature of such data is a challenge in analyzing and applying in a real-life health care environment.
2. Sources of health care data Datasets gathered in health care domains includes quantitative and qualitative data. Quantitative data is of quantifiable nature and used for comparison purpose. Examples include weight, age, temperature, or any other discrete variables. Qualitative data are nonnumerical in nature which is used to represent health related problems. Some examples include male/female or smoker/non-smoker etc. Data sources in medical field include scientific data and clinical data. Clinical data include data related to clinical surveys or epidemiological based information. Scientific data denotes data related to bench sciences. Data recorded and collected in medical domain are of primary and secondary in nature. Primary data refers to the individual person or a group to collect and analyze the data. This collected data may be used for research queries. Secondary data is dependent on the existing data which are already available and is utilized for other purpose. These data are used to answer research-based questions. Fig. 1.1 highlights the health care sources of data samples.
2.1 Electronic health records (EHR) This is an important source of data in medical field. Electronic health records (EHR) refers to the digital records of patients. Here the data can be accessed from anywhere and
Analysis of the role and scope of big data analytics with IoT in health care domain 3 Medical Imaging Data
EHR data
Clinical Text Mining
Sources of Health care Data
Genomic Data
Behavioral Data
Figure 1.1 Health care data sources.
whenever required. It may be structured or unstructured. In structured data, all records are properly captured and categorized in a database. But unstructured data records are vague and inconsistent which are presented in the form of static pages of health information. Examples include PDF files, emails, and digital images, audio, or video information. Ultimately these data are transformed into structured records.
2.2 Clinical text mining Health care records can be structured, unstructured, or can have text related information. Here text mining can be used to extract useful and sound information from huge raw data. Few text mining methods involve categorization and sentiment analysis. It is used for optimum targeting of drugs, precise disease diagnosis and efficient patient treatment. Natural language processing may be used in health care text mining.
2.3 Medical imaging data CT scans and X-rays belongs to unstructured data type. Picture Archival & Communication Systems is a system used to store and retrieve clinical imaging data records. In clinical retrieval process, images are deposited in repository of biomedical image data. These image-based data takes huge memory and is complicated in processing.
2.4 Genomic data These data handles DNA aspects in structural and sequential arrangement of various functionalities of genes. Specific software is required to store and process these data. A repository called genomic database comprises human genomes and association rules related to genomes. This repository determines the identical genetic symptoms influencing health and its associated diseases.
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2.5 Behavioral data The source of behavioral data lies in mobility-based sensor data associated with social network. There exist some social networks which keep track of diseases and their symptoms. Accordingly, they offer better treatment process based on the symptoms involved. Similarly, sensors can be deployed to gather and aggregate disease related data from patients in a health care institutions.
3. Tools and data analytics interfaces in medical and health care system There exist various tools and applications which are used to determine the progress in clinical data analysis. Some of the widely popular tools used are presented below.
3.1 Advanced data visualization (ADV) ADV is useful to deal with several types of data. It changes from line chart to standard bars. It is quite easy to use. It offers wide support to analysts in data exploration. It produces very optimum results and used to extract medical hidden patterns in health care data.
3.2 Presto Presto is a distributed SQL Query engine used in analyzing massive quantity of clinical data. It is applied in a large-scale analysis where data analysis can be done without significant delay.
3.3 Hive It is also applied to deal with large scale data records. It is not so fast like Presto tool. In fact it performs all Excel sheet tasks effectively. Many industries prefer Hive for medical records storage and retrieval.
3.4 Vertica This tool is identical to Presto and is utilized in processing huge amount of clinical based data which may be further used for data analytics. It is cost effective and its architecture is simple. It is very scalable in nature. It is advantageous in reducing operational costs, speeding up health care reports and documentation thereby helps in analyzing health patterns of patients.
Analysis of the role and scope of big data analytics with IoT in health care domain 5
3.5 Key performance indicators (KPI) This represents a procedure which makes application of electronic health care records in determining inventions and practices of human beings. Patients who are more vulnerable to hospital environment may be subjected to KPI tool to get better results.
3.6 Online analytics processing (OLAP) Here the data is organized in multidimensional patterns which perform statistical computation at a great speed. It amplifies data integrity constraints and establishes better quality control. It keeps track of health care records and helps in disease diagnosis.
3.7 Online transaction processing (OLTP) OLTP and OLAP are interrelated to each other. This tool is useful in processing registration of patients, analyze various operations of patients and result review analysis.
3.8 The Hadoop distributed file system (HDFS) The performance of clinical data analytics is improved by the use of HDFS which partitions huge data sets into relatively smaller ones. These smaller data samples are distributed across entire system. It removes redundancy of data. It acts as a diagnosis assisting tool and is used to monitor and detect fraud elements and patients symptoms.
3.9 Casandra file system (CFS) It is very much identical to HDFS. This file system is designed to handle analytical operations and is fault-tolerant.
3.10 Map reduce system This system deals with massive amount of data. It partitions the chore into subchores and aggregates its output. It efficiently integrates various operational computations into the system. It tracks every server where the chore is being done. The main benefit lies in its higher degree of parallel tasks.
3.11 Complex event processing (CEP) It is a recent addition in medical sector which helps in monitoring different phases of patient. Complicated event processing is interlinked in real-time analysis.
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3.12 Text mining In medical field text mining systems may act as an advantage in examining medical records from medical centers. It can be useful in devising treatment plans which can develop many protocols. Further treatment of patients can be undertaken relating to such developed guidelines.
3.13 Cloud computing Cloud computing technology offers higher flexibility in medical field as far as dealing with adaptive variations and health care updates are concerned. It is an addition to clinical sector by reducing the medical costs, enhancing the productivity an optimizing data analysis task.
3.14 Mahout Mahout is an apache-based project which is intended in developing applications to improve clinical data analytics on Hadoop systems.
3.15 JAQL JAQL is a procedure-oriented query language useful in processing massive amount of data. Parallel processing of data is feasible by converting higher level queries into lower level ones. It is well suited to work with Map reduce functions.
3.16 AVRO AVRO is effective in encoding and serialization of data. It enhances data semantics by specifying types of data samples used, semantics and its schema.
4. Health care with big data challenges The challenges can be categorized into two types:
4.1 Issues related to policy and fiscal factors In the age of money for service scenario, the medical experts can get paid only when they have a face to face interaction with their patients. It acts as a bottleneck to promote new technologies that encourage interaction without physical presence of patients. Moreover, as we go further away from direct interaction-based models, where there are more financial risks are involved there is more scope of using recent advanced technologies where
Analysis of the role and scope of big data analytics with IoT in health care domain 7 unnecessary face to face interactions may be avoided. In such cases, face to face interactions with patients are quite expensive while use of advance technologies impacts a positive influence in health outcomes of people.
4.2 Issues related to technology One of the largest technical obstacle to achieve this mission is the status of medical data. Developed by EHR systems, medical based data records are highly segmented into organization-based silos. Maximum effort is given to deal with this exchange of individual data records in between silos with the use of standard code sets and message structure. But it fails to solve the data fragmentation issue. Recently people in medical arena are visualizing the future generation of medical field lies in data aggregation and not just sharing copies of patient records. The data can be made relevant and useful only when data can be gathered from heterogeneous sources and further normalizing the gathered data and resolving the information with unique identifiers of patients. There are two main benefits of aggregated data. •
•
It resolves the interoperability issue. Organizations are no more required develop data bridges and convert the data between proprietary systems. They just need to connect data sources to a common API module. This data aggregation forms the basis of effective artificial intelligence technology. It provides adequate flexibility thereby allowing artificial intelligence and machine learning to operate efficiently in real-time manner.
5. IoT defined IoT refers to a computational notion to describe the concept of daily physical objects which are connected to internet such that they are able to identify and distinguish themselves from the rest. This methodology is acutely associated with RFID as the transmission technique. Besides this, it involves sensor and wireless technologies or QR codes. The significance of IoT lies in the fact that the digital representation of object becomes more visible that the object itself. The object is no more interrelated to its user but also is related to its neighboring objects. Some crucial focus areas where IoT analytics can be successfully applied are: • • • • • •
Forecasting the agriculture production/manufacture Machine learning algorithms Failure prediction Predictive maintenance Supply and chain Frequent pattern mining.
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6. IoT for health care The main aim of technology in medical field is to connect health care experts with their patients through smart devices. This helps patients to be more aware of their issues and thus the diagnosis becomes effective. It empowers consumer with less inefficiencies and assists doctor in precise decision making. IoT in health domain serves two main purposes which are: • •
Enhanced management of disease providing better patient experience. Decreased medical costs to make it more affordable for a wider demographic population.
As shown in Fig. 1.2, a survey was conducted by Grand View Research where it is estimated that by the year 2022, IoT in health filed which covers the domain of medical equipment, services, and software is presumed to jump a whopping $300B market expansion. Central agency schemes are also likely to impact and encourage this value for customized smart health care. Fig. 1.3 highlights the architectural view needed in clinical IoT systems. It consists of three prime components, which include the device layer, which has the body area sensor network embedded into it; Internet-connected smart local access network, which is called a Fog layer; and a Cloud layer for cloud and big data service support. Several applications and firms provide services to various stakeholders within the system by the use of this model. Sensors attached to users are responsible to generate data which is made readily available to medical experts, family members and authorized firms enabling them to verify and validate the issues and diagnosis process at anytime from anywhere as well as
Figure 1.2 Coverage analysis of IoT in health care services.
Analysis of the role and scope of big data analytics with IoT in health care domain 9
Figure 1.3 Architectural elements of health care IoT systems [1].
assisting health care experts in intelligent decision making. In this new age of information, knowledge extracted from raw data is the need of the hour. This is the age of customers where their associations with health care world are a priority. At this juncture, apps and devices will be applied to develop a health-aware environment. Some of these devices include: • • • • • • • • • •
OpenAPS: closed-loop insulin delivery Continuous glucose monitoring (CGM) system Activity trackers during cancer treatment Connected inhalers Ingestible sensors Connected contact lenses Depression-fighting Apple Watch app Coagulation testing Arthritis: Apple’s ResearchKit Parkinson’s Project Blue Sky
7. Challenges for IoT in health care Prime objective of reputed IoT firms is to provide simple and powerful implementations to services of IoT and data handling facility. It helps designers to compose data analytics applications, visualization frameworks and health care IoT apps. Some of the critical capabilities that IoT organizations must be enabled are: •
Simple connectivity: An ideal IoT firm must be competent enough to provide ease of connection to devices thereby facilitating device management functionalities.
10 Chapter 1 • •
•
•
Easy device management: It enables enhanced availability of different assets and resources which lead to improved throughput and reduction in maintenance costs. Information ingestion: Intelligent transformation and storage of data is a vital factor In IoT. Information is ingested from distinct sources of data and then relevant information is extracted with the use of data analytics. Informative analytics: Proper analysis of raw information is important for optimal decision making and smooth operations. It is used in real-time analytics and monitoring present conditions to respond accordingly. Moreover an intuitive dashboard makes it more simple and effective to understand. Reduced risk: Act on warnings and isolate activities collected somewhere in the organizations from a unit console.
8. Evolution of big data in medical IoT The health care industry is the combination of different sectors. The sectors has the inclusion of medicines, precautionary with the statistical working of the properties and the socialization of care. Health care industries have been included with different nursing home, medical trials, outsourcing, health coverage, telemedicine and other charitable organization. These days the health care industry has been seen from a business point of view. The business provides good profits and other services for the people. Along with this there has been an inclusion of information and communications technology. The ICT cell has been able to provide the health care industry with the provision of different roles for the improvement of health care industry. The system would be able to help the existing system in the exclusion of medical errors. Information related to the system is generated from different sources. Sources for the data includes clinical trials, medicine, exercise, variable symptoms, prescription, laboratory report, insurance data and various other information related to the patient and doctors. The large amount data in the health care industry is growing in an exponential form with current data size in the order of petabytes. This immense growth of data has given rise to various problems related to storage, transfer, and computational analysis. This form of data can be analyzed and processed with the help of traditional relational database system. Moreover, traditional database system can only process structured data. Whereas the data stored in the form of big data is unstructured. With the invention of new and efficient mechanism for the storage and the accessing of the information the ICT would be able to help serve the society in a better storage. The process of implementing of ICT in the health care industry in termed as eHealth. Thus the implementation in the health care industry would help in the processing of data and consecutive analysis and the improvement of the decision-making process for the collection of better treatment solutions for the symptoms for the diseases. One of the top characteristics of the use of health care industry is the richness of data. With the recent development of the diagnostics and the treatment processes, the health care industry has
Analysis of the role and scope of big data analytics with IoT in health care domain 11 been used to quickly evolve the sector in the previous couple of decades. Several sources are used for generation of big data and the sources are considered as following: •
• •
•
Web and social media: Captured data from Facebook, LinkedIn, Twitter, blogs, and shared stories on social media. The data captured from health planning webpages, smartphone applications and other sources as well. Machine-to-machine device generated data: Remote sensor data, meter readings and other device readings are recorded as machine-to-machine device generated data. Biometric data and demographic data: Biometric data such as capturing data from retinal scans, X-ray images, fingerprints, handwriting, blood pressure and other similar type of data can be recorded as demographic sources as well. Human-generated data: Unstructured and semistructured data can be captured from manual data entering and some data examples are considered as EMRs, doctor’s Prescription, and other paper documentations.
Big data concept represents a technology that stores and analyses datasets of massive size such as petabytes, terabytes, exabyte, and zettabytes. Such measure of data that is generally out of human expertise to store data in manual effort; capturing and analyzing the data can include significant value for business and decision-making process. Hence, organizations today are making the most from Big Data technologies to store process and analyze the data generated. Three important characteristics or the three Vs of big data are volume, velocity, and variety (Fig. 1.4). The data can be structured, unstructured, or semistructured. Structured data has a predefined arrangement of data; examples include
Figure 1.4 Three Vs (velocity, volume, and variety).
12 Chapter 1 Healthcare Services Payors & Providers
101010101010 001000100010 001100110011
Massive Data Flows: Clinical Claims, Lab, Rx, Demographics, Benefits, DME, EHR, Clinical, Patient-Reported, and Supplemental Data Sources
Big Data Analytics Processing
Pharma Integration & Interoperability Impact Enablement
Devices
Diagnostics The MORE2 Registry© Database: Proprietary Design, Research and Functionality
Patients & Consumers
Figure 1.5 Big data analytics in health care: key components.
address data books, product information catalogs, and banking data. Unstructured data do not have predefined arrangement; examples include audio files, video files, text records, web sheets, computer programs, and social websites. Semistructured data is neither fully structured nor fully unstructured, and 80% of data is unstructured. Big data is interrelated with analytics and analytics is a familiar concept. Regression methods, simulation, and machine learning algorithms are the analytic techniques that were in use from past several years. The analytics carried out using the above-mentioned techniques for the unstructured data like email, computer programs and documents is understandable. However, the new thing is, as said above the advanced sources in computer technology and software’s like social media sites such as Facebook, Twitter, blogs, and many other business opportunities are generating enormous amount of data every minute. This has given rise to move toward advanced technology called Big Data analytics that supports many tools and technologies for the data analytics. Apart from just analyzing, the huge data there are many other benefits from big data technologies. Fig. 1.5 illustrates the key components of big data analytics in health care.
9. Advantages Here various benefits associated with big data in medical field are discussed. •
Reducing medical costs to achieve financial benefit: Cost of medical treatment can be significantly reduced with big data analytics. Also efficient data analysis provides information to physicians to determine populations at risk for ill-health. Hence proactive actions may be taken at prior. Big data can be applied accurately to analyze the scenarios where education and preventive actions are needed to get more healthy
Analysis of the role and scope of big data analytics with IoT in health care domain 13
•
•
•
•
•
•
populations at less cost. Treatment becomes more evidence related on applying big data analytics. Personalized medicine: Through genetic blue prints, medical experts can accurately predict the diseases and its risk factors. Big data further helps in personalization of medical drugs by determining proper treatments of patients. Patient treatment at early phase can reduce the risk of chronic disease factors. Strengthening preventive care: Prevention mechanism is far better than cure. With this rule, applying big data it is simpler to capture process and analyze symptoms of patients to offer a preventive care in an optimal manner. Wearable medical technologies: With the advance in technology, medical experts make better use of wearable devices thereby enhancing the quality of care and providing patients with more accessibility. Clinical trend analysis: By the usage of various analytical techniques which include machine learning and text mining, medical trend analysis and management of patients data become simpler with big data analytics. Detection and tracking patients: Various big data analytics methods like statistical clustering can be used to cluster group of patients suffering from different diseases. This technique applies available data samples. Also, patients can be tracked precisely to identify the regular patterns for treatment of disease. Analyzing efficacy of drugs: Studying drug efficacy may be done with electronic health record data. A comparative analysis of EMR data and hospital medical records are done and it is observed that cost of trials at random is higher than that of readily available HER based data records to analyze treatment procedures.
10. Literature survey Pagan et al. [2] in their work studied a power-aware huge deployment of a body area network to predict migraine activities of patients across Europe. It aimed to address issues in data acquisition and analysis in clinical sector. Adame et al. [3] developed an IoT monitoring model that combines both sensor network and RFID technology. It helps in tracking the location of medical resources and assets. It also keeps the patients informed about their body temperature, heart rate and movement. It is practically implemented in a hospital environment using back end servers with qualitative feedback analysis. A hybrid technique to sense and monitor locomotive movements by determining variations in wireless signal strength is proposed by Ammae et al. [4]. With the change in signal strength, it allows an unobtrusive method of determining the quality of sleep using maximum likelihood linear regression model. It was evaluated with the usage of 60 iterations of real-time information gathered from 6individuals and the result obtained was very positive and efficient. Woo et al. [5] focused on fault-tolerant medical data services by developing a fault-tolerant and reliable IoT system model. Here the gateways are
14 Chapter 1 interlinked to develop a daisy chain of reliability where the replica of the predecessor gateway can be stored in the daisy chain. It enabled the user to simultaneously recover from a two gateway faults. Rahmani et al. [6] presented a smart e-health care gateway at the network edge in an architectural configuration of a fog computing. Farahani et al. [1] in his article discussed regarding the challenges and scope of IoT in the health care field. A system model was proposed that migrated from hospital-oriented centers to people oriented centers with the help of IoT infrastructure. In Refs. [7,8], an IoT framework for elderly persons was proposed and implemented by observing several physiological features. An IoT-based intelligent wallet system was developed in Ref. [9] which is associated with every individual in order to store signals and wallet shares. A wireless sensor network based IoT model was presented in Ref. [10] that reduced the power consumption at the sensor nodes which enhanced the network lifetime [11]. discusses the impact of IoT concept with respect to the overall geographical range. Developed countries are rapidly progressing in IoT field but it is the less developed regions where IoT technology is crucial for the overall development of humanity. Authors in Ref. [12] developed a heart monitoring gadget with the help of remote sensors and advanced smart phone technology. It sends an alarming signal to the medical experts and family members in case any discrepancies are observed in the patient. The security and privacy concerns in IoT can be properly addressed by the usage of body sensor network technology which is illustrated in Ref. [13]. Authors in Ref. [14] present an elaborate analysis on the collaboration of cloud and IoT together. These two technologies can work together to offer efficient health care monitoring system model where huge amount of data can be processed and results can be obtained in real time more accurately [15]. developed and analyzed a pervasive surveillance system with mesh topology which is used as data compression processing system for patients in hospitals. Data are aggregated and stored in cloud server. Real-time data in the form of images and video are generated and analyzed. Any variation in data recorded is immediately monitored and appropriate actions are taken on patients [16]. discusses the significance of data quality and reliability as two important factors in health careebased IoT applications. The clinical analytics with the use of an effective anomaly identification model was proposed to detect any early and prompt inconsistencies of dominant diseases. Authors in Ref. [17] proposed a health care system for elderly patients. It employed a central unit model which facilitated in decision making which could detect any critical anomalies in elderly people based on the data generated by the sensor units. At the end of analysis in emergency scenario, the system model transmits an alert signal to an emergency control unit. Sushruta Mishra et al. [18] proposes a machine learning-system model based on biologically inspired computation for primary tumor classification. Genetic algorithm was the attribute optimization technique used in the study. Authors in Ref. [19] presented a social network analysis framework for big data analysis on telecommunication domain. similarly various research works are being carried out in relation to big data analytics and IoT filed with respect to medical data analysis.
Analysis of the role and scope of big data analytics with IoT in health care domain 15
11. Implementation of a real-time big data analytics of IoT-based health care monitoring system Complicated tools integrated with IoT are fruitful for the medical experts in monitoring huge health care related data records so as to keep track of their patient’s health condition. According to the signals transmitted by the IoT system, health status of patients is continuously tracked and in case of any abnormalities, alert signals are sent to the physicians. For example, if the glucose level of a patient drops, the system will transmit an alert message to the doctor who can interact with his patient and take appropriate actions. This data is aggregated from patients and deployed in cloud. The cloud environment makes it feasible to gather and aggregate data rapidly and more accurately. The proposed system model integrates various entities like medical amenities, physicians, vital medical services, patients and other technologies to extract the relevant value form the gathered medical data records. Salient features of the proposed system model include the following: • • • • •
Sensors are used to collect health data of patients which are further used for processing and communication purpose. Intel Galileo Gen2 which acts as medical IoT agent are utilized in analyzing sensor related data and used for storage in cloud for analysis. Data analytics of medical related records with the help of the map reduce technique in cloud. Graphical user interfaces controlled by medical monitors for smooth progress. Smart phones embedded with GPRS and GSM connections.
Big data analytics for health care based IoT architecture is represented in Fig. 1.6. The health proxy played by the IoT agent is coupled with the heart rate sensor, blood pressure sensor and humidity sensor. The medical metrics sensed by the system model are employed and integrated within cloud. The cloud concept is used here to analyze such huge medical data generated from various patients across distinct medical organizations. Historical data analysis of medical information for patients is feasible with the stored data in cloud. The physician’s smart phone is used to connect directly to the IoT agent through GPIO pins and collect health careebased data of patients. Appropriate medical help may be provided if any critical medical condition is identified in patients. The proposed health care monitoring model with IoT can also be further integrated with smart phone interfaced with it. The system model shown in the figure offers connectivity to cloud thus delivering end-to-end customer value. Sensor data analytics are executed inside the cloud using HDFS file system and MapReduce method. It is used for storing and processing health care related sensor information.
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IOT Agent
IOT Analytics HTTP
GPRS/GSM
Figure 1.6 Proposed IoT architecture.
11.1 Components and methods The developed clinical monitoring model used various types of sensors such as finger moisture sensors, blood pressure sensors and heart beat rate sensors. These sensors collect and aggregate data from patients. This health-monitoring model uses various physiological signals generated from patients. Intel Galileo which acts as an IoT agent is an IBM registered device is selected for implementation purpose. Sensor data values are recorded with the use of Arduino programming. With the help of Intel Galileo Gen2, interfacing with outside world is also feasible. Fig. 1.7 illustrates a heart beat sensor while Fig. 1.8 shows moisture sensor implemented in our developed work. A PCB antenna designed with an industrial based standard interface. ATWIN Quad-band GPRS/GSM shield acts as a wireless module base, which is of very high quality performance based on UCL2 interface. It is a small dimension-based SMT package with less power consumption with a double-band package. It has provision of SMS, voice, data, and fax applications for medical experts. It is shown in Fig. 1.9. • • • •
IoT Proxy’s D0 is connected to Rx of hardware serial port IoT Proxy’s D1 is connected to Tx of hardware serial port IoT Proxy’s 5V are connected to 5V of hardware serial port IoT Proxy’s GND is connected to GND of hardware serial port
Analysis of the role and scope of big data analytics with IoT in health care domain 17
Figure 1.7 IEEE 802.15.4 sender and receiver with heart beat sensor.
Figure 1.8 Intel Galileo Gen2 with moisture sensor.
An unlocked mini-SIM (size of 2 FF) card is associated within it. The IoT Proxy lies in the shield. No extra wiring is necessary. The Serial port select jumpers to the hardware serial position are set as follows: • •
Set J1 to connect Rx to MTx Set J2 to connect Tx to MRx
Fig. 1.10 shows the SIM card inserted into Intel Galileo. The components required for the Intel IoT Developer Kit used in this paper needs to connect the IoT proxy (Intel Galileo Board) over Wi-Fi connection. Through available miniPCIe slot IoT agent provides cloud based connectivity with sensors supporting Wi-Fi service which leads to development of IoT health-monitoring model.The IoT agent is able to abstract the complexities of the
18 Chapter 1
Figure 1.9 SMT package inserted into Intel Galileo with PCB antenna.
Figure 1.10 SIM card integrated to Galileo Gen2.
connectivity to the cloud. This process helps in the focusing of the application development according to the proposed solution including the health care sensors. The agents’ works in the formatting of data and the security protocol for registering sensors in cloud. With provision of end-to-end service of IoT solution from Intel, it has been implemented in the proposed system. The proposed system would have quick management, connectivity, and protection of the sensors. Physiological metrics like heart beat levels are gathered from sensors and are used for storage on a web cloud server for processing. “Thingspeak” cloud is used in this implementation work. The readings of finger moisture level are recorded at regular intervals and are shown in Fig. 1.11. The medical sensor data records generated in
Analysis of the role and scope of big data analytics with IoT in health care domain 19
Figure 1.11 Deployment of finger moisture in Thingspeak cloud.
Thingspeak cloud are mapped onto Hadoop Distributed File System (HDFS). HDFS acts as the storage for Hadoop cluster. The medical data is split into small divisions and are made available across several servers in cloud. Map reduce programming is used to perform the computational distribution of sensor data. Data is gathered by distributing various subtasks in each server. In the cloud environment, all the server nodes are tracked with the use of map reduce. The map reduce technique with its classes and methods split the large data file into relatively smaller divisions which are further mapped in the cloud. These smaller data partitions are parallel executed to determine the final output. As a result the execution time is reduced. Finally the result of these sensors of map reduce is published to all mobile phones with medical experts after appropriate authentication.
11.2 Results and discussion The patients act upon the heart beat arte sensors and the IoT device records the data. This data is implemented with Thingspeak cloud. This procedure is highlighted in Fig. 1.6. Also the recordings of moisture sensors are displayed. A command tool called Hive is used to retrieve the data recordings of body sensor. Fig. 1.12 depicts the data analytics gathered from the body pressure with Hive query command. It is clearly seen that the response time period of the proposed health care system is very prompt as the completion of the query using Hive command requires only 1.05 s after the Map Reduce processing is over in the cloud, the aggregated sensor information is transmitted to the health care
20 Chapter 1 hive> CREATE TABLE xml7(Level map
ROW FORMAT SERDE
'com.ibm.spss.hive.serde2.xml.XmlSerDe' >
WITH SERDEPR0PERTIE5 (
>
"column.xpath.Level"="/Units/Level",
>
"column.xpath.Pressure"="/Unlts/Pressure"
>
)
>
STORED AS
>
INPUTFORMAT 'com.ibm.spss.hive.serde2.xml.XmlInputFormat'
>
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveOutputFormat' >
TBLPROPERTIES (
>
"xmlinput.start"="
>
Figure 12. Body pressure with Hive query command "xmlinput.endn="
>
);
OK Time taken: 1.056 seconds
Figure 1.12 Body pressure with Hive query command.
monitoring system which is highlighted in Fig. 1.13. The smart phone device may be connected to the interface directly. Apart from this, the IoT agent can also communicate with the smart phone by the usage of SMT package and SIM card of the device. This condition is illustrated in Fig. 1.5. The alerting model is developed which is based on the optimal threshold computation which is seen in Fig. 1.14. Once arrhythmia is diagnosed in patient, the alert system immediately sends an alert signal. The model reads the physiological metrics which provides the alert signals after the threshold value is compared. A self-adaptive alert system has been included in the proposed health care system which generates notifies the medical experts about their patients in case any emergency arises. The threshold data values concerning the heart beat rate alert signals are illustrated in Table 1.1.
Analysis of the role and scope of big data analytics with IoT in health care domain 21
Figure 1.13 Health-monitoring Interface.
If HR > 100
No
If HR < 60
No
HR is normal
Yes
Yes
Tachycardia
Bradycardia
Arrhythmia detected
Figure 1.14 Flow chart for alert.
Table 1.2 depicts the data gathered from the alert system model implemented in the proposed health care system. A comparative analysis was carried out and it was determined that the heart beat monitoring has been enhanced with the proposed model. It is measured that the average time taken to transfer the alert signal between the sender and the receiver in presented IoT model is found to be 35 s and this time is within 3G network recorded in Table 1.2. As per big data analytics requirements, the overhead is performed on the heartbeat rate, and thereafter the alert signal is raised.
22 Chapter 1 Table 1.1: Threshold data of heart beat rate alert signals. Sinus rhythm type
Threshold data of heart beat rate
Normal Tachycardia Bradycardia Sinus rhythm type Normal Tachycardia Bradycardia
60 < HR < 100 (beats/minute) HR > 100 (beats/minute) HR < 60 (beats/minute) Threshold value of heart rate 60 < HR < 100 (beats/minute) HR > 100 (beats/minute) HR < 60 (beats/minute)
Table 1.2: Average data transmission time.
Alert for
Mean time in send and receive alert in Wi-Fi (H:M:S)
Tachycardia Bradycardia
00:00:29 00:00:30
Mean time in send and receive alert in 3G network (H:M:S) 00:00:58 00:00:59
Mean time in send and receive alert in proposed IoT system (H:M:S) 00:00:35 00:00:38
12. Conclusion More new technologies are rapidly becoming useful in the health care sector, including devices and models that regularly monitor health parameters and other devices that keep track of real-time medical information. Due to increases in Internet speed and the wide availability of smart phones, patients as well as doctors are using mobile-based applications to regulate their health requirements. Integration of big data analytics with IoT technology plays a vital role in this health care domain. In this chapter we have drawn a detail analysis and discussion about the role and scope of big data analytics and IoT technology in medical field. Later a case study illustrating an IoT-based medical monitoring system model with Big data analytics is presented and its implementation is also highlighted. Here huge quantity of heartbeat data records of patients are gathered and respective medical expert is required to segregate the data according to patient. Intel Galileo Gen2 is the IoT-based agent used in the above implementation to integrate clinical data of concerned patients with Thingspeak cloud. Hadoop framework is applied to process this massive data of patients over cloud. It was observed that the response time was much less without significant delay. Also, it can be deployed in real-time environment for health care monitoring of patients. Clinical metrics coordination is done with the usage of GPRS/GSM connection abilities of Intel Galileo Gen2 through alert signals. Physicians can take benefits from this developed health care system providing appropriate information to appropriate patient at appropriate time. Hence, effective and timely diagnosis can be provided to patients with minimum response time.
Analysis of the role and scope of big data analytics with IoT in health care domain 23
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