A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection

Expert Systems with Applications 39 (2012) 3995–4006

Contents lists available at SciVerse ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection Rung-Ching Chen a,⇑, Yun-Hou Huang b, Cho-Tsan Bau a,d, Shyi-Ming Chen b,c a

Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan c Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung, Taiwan d Division of Endocrinology and Metabolism, Department of Medicine, Taichung Hospital, Department of Health, Taichung, Taiwan b

a r t i c l e

i n f o

Keywords: Ontology OWL SWRL JESS Recommendation system

a b s t r a c t Diabetes mellitus is one of the most common chronic diseases in recent years. According to the World Health Organization, estimated diabetic patient numbers will increase by 56 percent in Asia from the year 2010 to 2025. Mean while, the number of anti-diabetic drugs that doctors are able to utilize also increase as the development of pharmaceutical drugs. In this paper, we present a Diabetes Medication Recommendation system, based on domain ontology, that employ the knowledge base provided by a hospital specialist in Taichung’s Department of Health and the database of the American Association of Clinical Endocrinologists Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus (AACEMG). By thorough analysis, the system first builds ontology knowledge about the drugs’ nature attributes, type of dispensing and side effects, and ontology knowledge about patients’ symptoms. It then utilizes Semantic Web Rule Language (SWRL) and Java Expert System Shell (JESS) to induce potential prescriptions for the patients. This system is able to analyze the symptoms of diabetes as well as to select the most appropriate drug from related drugs. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Clinical decision support systems (CDSS) have been utilized in past 40 years. The first generation clinical decision support system is the MYCIN system. MYCIN is an infectious disease distinguishing diagnosis system, which was developed in the 1970s; Quick Medical Reference system (QMR) was developed in the 1980s (Shortliffe & Hance, 1974). MYCIN data of input was done by a serial of inter face conversations to get series data to distinguish infectious disease diagnosis as well as antibiotic dosages and treatment types. The QMR is a medical diagnosis expert system for personal computers. It has 85% accuracy rate of diagnosis (Coronato, Esposito, & Pietro, 2009). An expert system includes domain experts, programmers and knowledge engine. The knowledge engine uses knowledge acquisition tools to solve the problem between knowledge and the program from domain experts. Knowledge representation schemes are used to store knowledge. The logical deduction and inference in knowledge base are done by inference engine to get results (Huang, 2003). Ontology is one of the import technologies of expert systems.

⇑ Corresponding author. Tel.: +886 4 2332 3000x7701. E-mail address: [email protected] (R.-C. Chen). 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.09.061

Ontology technologies have different applications utilizing search domain or decision system in recent years. The Europe Union also supports an ontology-based framework on constructing of domain knowledge for cardiovascular disease (Jovic, Prcela, & Gamberger, 2007). Ontology is a combination of Artificial Intelligence (AI) and machine language to share and reuse knowledge. It also contains natural language processors, knowledge representation, etc. Ontology is used to communication between human behavior and computer system. Ontology is further availed in information retrieval and knowledge management (Zhang, Jia, & Wang, 2010). In order to retrieve accurate information, the framework of domain ontology must be clear. The statistics of International Diabetes Federation (IDF) shows that more than 246 million people have diabetes in the world. If this trend continues, the numbers will happen to 380 million by 2025 (http://www.doh.gov.tw/cht2006/index_populace.aspx). According to the Department of Health the statistics, in 2006 about 4.3% of people living in Taiwan had diabetes. As patient ages increase, so do the number of people diagnosed with diabetes. Of the population over the age of 20, 5–9% suffered with diabetes; over the age of 40, 11–13% suffer; and of the population over the age of 60, more than 20% have diabetic problems. Unfortunately, diabetes has been the fourth leading cause of death in Taiwan (http://www.doh.gov.tw/cht2006/index_populace.aspx) and it is

3996

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

becoming an upward trend, making this an important issue. There are many kinds of anti-diabetic drugs, and the number increases frequently. In order to get the best treatment for diabetes, it is necessary to develop a recommendation system for anti-diabetic drugs for clinicians to use. There are two major categories of anti-diabetes drugs, oral hypoglycemic agents and insulin. Insulin is the most effective glucose-lowering drug, but it must be administered with injection, which patients usually refuse to use (http://www.doh.gov.tw/ cht2006/index_populace.aspx). It is easy for patients to take oral medicines and it not have to endure the pain of injections, making oral hypoglycemic agents the first choice for patients. In general, diabetics need 2–3 kinds of oral hypoglycemic agents to control blood sugar. However, determining how to combine these oral hypoglycemic agents is a complex task. It is difficult for general practitioners and the incidence of adverse drug reactions will increase if physicians do not take careful consideration when prescribing drugs. Therefore it is necessary to design a recommendation system for physicians to use. Concurrently, it is not easy to obtain desired information from the database of antidiabetic drugs quickly. It is therefore necessary to develop a recommendation system depending upon ontology. In this paper, we adopted the ‘‘Medical Guidelines for Clinical Practice for The Management of Diabetes Mellitus’’ provided by American Association of Clinical Endocrinologists with the help of an endocrinologist’s doctor at a hospital in Taichung, Taiwan. The nature attributes, class, contraindication and side effect of the drugs were collated and analyzed completely to build ontology. The anti-diabetic drugs ontology was constructed to speed up the medicine retrieval process. SWRL and JESS were combined to analyze diabetes mellitus information allowing for the most appropriate drugs to be suggested. The system will bears independency of platform, excellent expansion, and high-efficient.

2. Literature review The application of content-based anti-diabetes drugs knowledge ontology combines with Semantic Web Rule Language (SWRL) to build the diabetes medicates association rules. The researchers propose an Anti-Diabetes Drug Recommendation System based on content based drug retrieval. This section focuses on the study of related works on recommendation system and system interface for information retrieval.

2.1. Expert systems Newell and Simon developed the first expert system ‘‘The logic theory machine-a complex information processing system’’ in 1957 (Newell, Shaw, & Simon, 1957). Artificial Intelligence and Expert System programming language and software exploitation tools are gradually developed which help expert systems succeed development (Bobillo, Delgado, Gómez-Romero, & López, 2009; Prcela, Gamberger, & Jovic, 2008). For example, in medicine, Stanford University developed a chemical structure of the Dendral inference system; Dombal proposed a medical diagnostic system that targets acute abdominal pain, and Shortliffe and Hance proposed a MYCIN infectious disease distinguishing diagnostic system (Shortliffe & Hance, 1974). These systems prove to be successful examples. Expert systems have been widely used in many fields. Rule-based expert systems have five major components: knowledge base, database, inference engine, explanation facilities, and user interface (http://www.cstp.umkc.edu/leeyu/class/CS560/ Lecture/lect-rule1.pdf).

1. Knowledge base: The knowledge base stores the domain expert knowledge that is useful for domain problem solving. The knowledge is represented as a set of rules in a rule-based expert system. 2. Database: The database includes a set of fact used to match against the IF (condition) rules stored in the knowledge base. 3. Inference engine: The inference engine reasons Knowledge base (rule) and Database (facts) to get results. 4. Explanation facilities: The explanation facilities enable the user to ask the expert system how a particular conclusion is reached and why a specific fact is needed. 5. User interface: The user’s system aims to provide the user with an application interface. A graphical communication interface allows users to close the system more. In recent years, recommendation system and expert system were mainly developed based on domain knowledge and problem-solving methods, including knowledge that was shared and reused. The recommendation system and expert system used ontology to solve classification, annotation, rendering and to format different interpretations that make knowledge representation efficiently. 2.2. Ontology The earliest idea of ontology derives from Aristotle where Metaphysics is the knowledge of exploring the being (http://en.wikipedia.org/wiki/Ontology/). It mainly describes the existence of instances or things in the real world. In recent years, ontology is often used in the field of Computer Science and Artificial Intelligence (Reformat & Ly, 2009; Shue, Chen, & Shiue, 2009). Gruber (1993) defined ontology as the ‘‘An ontology is an explicit specification of a conceptualization’’. Ontology is expressed by the aggregation of conceptualizations and a systematic description. Choi et al. classify ontology as three types in ontology: Global Ontology, Local Ontology, and Domain Ontology (Andreasen & Bulskov, 2009; Chen, Liang, & Pan, 2008; Choi, Song, & Han, 2006). In this paper, we will construct an domain ontology. Ontology is used to clearly describe the concept in a field, the characteristics of properties, attributes, as well as specific restrictions relevant to the concept described. Ontology includes concepts, relationships and instances (Baorto, Li, & Cimino, 2009; Lee, 2009; Snae & Brueckner, 2009) listed in the following: 1. Concept or Class: A series of concepts that represent topics or characters in the domain ontology. 2. Relationship or Attribute: Relations between concepts when considering a specific concept. 3. Instance: A series of concepts and relationships that have specific knowledge, such as web pages, documents and so on. For example, ontological elements included class, attribute, and instance in this study. Class refers to a category or concept, such as diabetes, anti-diabetic drugs, side effects, and testing. Each one can be called a class. Attributes in the ontology are used to describe the relationship between concepts. Instances in ontology are a case of concepts or categories. The instance will inherit all the attributes or relationship of their class. 2.3. Web Ontology Language (OWL) OWL is the framework proposed by W3C (Argüello & Des, 2007). OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDFS) by providing additional vocabulary along with a formal semantics (Yang, Miao, Wu, & Zhou, 2009; Yang, Miao, Wu, & Zhou, 2009). For

3997

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

example, rdfs:subClassOf in OWL can still be used. RDF lacks the description of relationships, which can be complemented by OWL. OWL can be divided into three levels of language: OWL Full, OWL DL, and OWL Lite. 1. OWL Full: is the maximum expressiveness and the syntactic freedom. However, the RDF base has not computational guarantees in OWL Full. OWL Full allows an ontology to augment the meaning of the pre-defined (RDF or OWL) vocabulary. It is unlikely that any reasoning software will be able to support complete reasoning for every feature of OWL Full (http://www.w3.org/TR/owl-features/). 2. OWL DL (Description Logic): is a sub-category of OWL language. OWL DL supports those users who want the maximum expressiveness while retaining computational completeness (Rubin, 2008) (all conclusions are guaranteed to be computable) and decidability (all computations will finish in finite time) (Temal, Dojat, Kassel, & Gibaud, 2008) (http://www.w3.org/TR/owl-features/). 3. OWL Lite: OWL Lite provides a quick migration path for thesauri and other taxonomies. It only permits cardinality values of 0 or 1 (http://www.w3.org/TR/owl-features/) and OWL Lite supports those users primarily needing a classification hierarchy and simple constraints.

2.4. Semantic Web Rule Language (SWRL) The SWRL was used to describe the relationship between the rules. SWRL is based on semantic rules (Chi, 2009). Rules of the SWRL are evolved from Rule Markup Language (RuleML) (Grosof, 2004). SWRL rule has an antecedent part and a consequent part to reason where the results combined with OWL ontology. SWRL is a specification of W3C at present time. The basic pattern, which used instance to express the inferential results and concept and relationship the inferential premise in RuleML, was also retained in SWRL (http://www.w3.org/Submission/ SWRL/). SWRL may be regarded as the combination of rules and ontology, through which the relationships and terms described in ontology can be used directly when writing rules. At first, the relationship between these classes would have additional rules to be described, but the description in ontology may be used directly in SWRL (Mabotuwana & Warren, 2009). There is a relationship of cause and effect between them. Fig. 1 indicates that if x has parent y, x has brother z, based on SWRL to build the rule, then z is y’s uncle.

2.5. Java Expert System Shell (JESS)

JESS also added a number of features such as backward reasoning, running memory checks, and the ability to operate and direct reason Java objects. In Protégé, the FuzzyJ Toolkit provides the capability for modeling fuzzy concepts and reasoning in a Java setting. Much of the work is based on earlier experience with the FuzzyCLIPS extension to the CLIPS Expert System Shell (Orchard, 2001). FuzzyJESS is an extension of JESS which enables the use of fuzzy rules. Fuzzy concepts are represented using fuzzy variables, fuzzy sets and fuzzy values. The logic of expert systems is often encoded in rules. In the FuzzyJ Toolkit, these are fuzzy rules. A FuzzyRule holds three sets of FuzzyValues representing the antecedents, conclusions and input values of the rule. A rule might be written as follows: If then

a1 and a2 {C1, C2}

...

and

an

The antecedents (a1) are the premises of the rule that must be true before the rule can be executed, and (c1) of the rule can be shown. 3. Research framework The recommendation system of anti-diabetic medication is developed for doctors to use. The experimental data are the diabetic patient’s conditions and the endocrinologist’s expertise must be trustable. The recommendation system for anti-diabetic drugs is used to recommend drugs more suitable, it avoids doctor conditional. The restrictions on anti-diabetic drugs are expectant decline in HbA1c (Glycated hemoglobin) levels, safety, side effects, tolerance, convenience, long-term compliance and other effects beyond lowering blood sugar. AACEMG for clinical practice for the management of diabetes mellitus (Rodbard et al., 2007) was used as the criteria. Antidiabetic drugs ontology was built systematically with the help of endocrinologist. The aspect of implementation was anti-diabetic drug ontology with reasoning ability and retrieval functions. Fig. 2 is the structure of the recommendation system for antidiabetic drugs. 1. The knowledge base of anti-diabetic medicine ontology and patients test ontology were used to support the normative framework for ontology. Protégé was used to build the antidiabetic medicine ontology to store the regulations of antidiabetic medicine. The main constituent elements of ontology are classes, attributes, and relationships. Classes represent the

Friedman-Hill et al. proposed JESS (Java Expert System Shell) which is a rule engine and scripting environment written entirely in Java (O’Connor, Knublauch, Tu, & Musen, 2005). It was built on a powerful Java environment without compiling any Java code. Core JESS language and CLIPS are compatible. CLIPS uses Rete algorithm to process rules like JESS. Rete is a very efficient algorithm (http://herzberg.ca.sandia.gov/). It is used to solve many-matching problems, both complex and difficult. It is known for its fast rule inference engine.

Fig. 1. An example of SWRL.

Fig. 2. The structure of recommendation system for anti-diabetic drugs.

3998

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

Table 1 The knowledge of anti-diabetic drugs.

2.

3.

4.

5.

6.

7.

Medicine class

Medicine name generic (Brand)

Composition

Contraindication

HbA1c, %

Sulfonylureas

Glibenclamide (Euglucon) Glipizide (Minidiab) Gliclazide (Diamicron) Glimepiride (Amaryl)

Glyburide (2.5 mg)(5 mg) Glidiab (5 mg)

1. Pregnant woman 2. Liver or renal failure

0.9–2.5

Biguanides

Metformin (Glucophage) Buformin (Bigunal) Pioglitazone (Actos)

Metformin HCl (500 mg) (850 mg) Buformin HCl (50 mg) Pioglitazone HCl (30 mg)

1. Urine toxins, cardio-pulmonary insufficiency, alcoholism, liver and kidney functions were very bad 2. Pregnant woman 3. Elderly

1.1–3.0

Meglitinide

Repaglinide (NovoNorm) Nateglinide (Starlix)

NovoNorm (0.5 mg) (1 mg) (2 mg) Nateglinide (60 mg)

1.Type I diabetes 2. Destruction of the pancreas of diabetes patients 3. Luo diabetic patients suffering from major diseases 4. Insulin injection therapy patients

0.8

...

...

...

...

...

Gliclazide (30 mg)(80 mg) Glimepiride (1 mg)(2 mg)

concept of domain knowledge. Attributes describe the attribute of classes. Relationships describe the relationship between class and class. SWRL is an ontology-based rule language. The rules of medicine regulations were retrieved from the AACEMG. The rules indicate the following: which oral hypoglycemic agent may be used when the HbA1c level is between 6.5% and 7.5%. What the side effects are, what the contraindication is, and what needs to be monitored will be. Through the inference engine (Pellet), the knowledge class and concept explanation in anti-diabetic medicine ontology were translated into the format that the recommendation system can accept, and then the medical instance may go into operation. The reason is that the instance in ontology cannot be used when making real inferences about rules. In addition, the inference engine recognizes the conflicting and contradictory knowledge in ontology. Since SWRL cannot do direct operation, the format needs to be changed through XSLT. However, Protégé 3.4.4 established SWRL directly and created rules, which were converted through SWRL2JESS to JESS in an acceptable format for this study. The information, knowledge, experience, and informational rules generated during the process of reasoning were stored in the working memory area temporarily. JESS is used for reasoning to identify which agents meet the test results. The instance of contraindication, side effects, and what needs to be monitored may be displayed. The user’s system aims to provide the doctor with an application interface. A graphical communication interface lets users operate the system easily.

3.1. Construction of knowledge system Our study used Protégé to build medicinal ontology. Table 1 is scientific name, composition, contraindication and reduction in HbA1c of oral hypoglycemic agents. Anti-diabetic medicine ontology was constructed according to the AACEMG for clinical practice for the management of diabetes mellitus (Rodbard et al., 2007). Protégé is on open software and it uses Java-based graphical applications. Protégé was developed from the Information Center

Fig. 3. The processing steps of domain knowledge.

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

3999

Fig. 4. Medicine ontology was created by Protégé.

at Stanford University and is used to access, create and maintain the ontology. Protégé supports RDF (Resource Description Framework). RDF is used to describe the correlation between web pages and other resources, and support the main ontological components, classes and attributes (Guo, 2008). Class represents the concept of knowledge. Attribute mainly describes the attributes and relationships of class linked to the basic data types. The relationship can link another Instance or Class. Classes, attributes, domain, and scopes are used to describe data. Protégé was used to construct ontology. The available language for ontology includes RDF, OWL, and XML Schema (http://en.wikipedia.org/wiki/Ontology/). Protégé is written in JAVA programming language, which strengthens the expansibility of ontological application environment, and provides rapid compilation to establish ontology. Fig. 3 shows the pre-processing about anti-diabetic medicine divided into two sub-processing sections, as listed below:

tion, scientific name and the dosage of the anti-diabetic medicine. Monitorization and contraindications are also attributes that are considered. 2. Test for diabetic patients: The tests for diabetic patients can be divided into three major tests classes. In our study, HbA1c was used as the major class among the tests. Other test classes included Liver test, and Renal insufficiency test. Fig. 4 is the anti-diabetic medicine ontology created by Protégé. The left frame represents anti-diabetic medicine knowledge and diabetic patient tests and the right frame designates the class of restraint. Fig. 5 shows the whole processing of anti-diabetic drug rules. Table 2 shows HbA1c of the AACEMG; Table 3 shows weights

1. Information extraction: The information includes patients’ HbA1c tests, Liver tests, Renal insufficiency tests, and antidiabetic medicine names, ingredients, side effects. The drugs’ data was not only extracted from a single database but also from many medical documents. 2. Protégé builds anti-diabetic medicine knowledge: Protégé was used to construct the medicine ontology in the preliminary experiment, and then the OWL DL format was adopted. In our study, ontology was constructed in the OWL DL (Description Logic). The classes of medicine, attributes of medicine class, and contents of medicine properties were set up to construct the relevant knowledge of anti-diabetic drugs. The experimental process of knowledge ontology construction was shown as the following: 1. Anti-diabetic medicine knowledge: Diabetic drugs can be divided into six major medicine classes, including Sulfonylureas, Biguanides, a-glucosidase, Thiazolidinedione, Meglitinide and DDP4 inhibitor. Sub-classes include medicine name, ingredients and side effects. Medicine properties indicate composi-

Fig. 5. To construct anti-diabetic drugs rules on SWRL.

4000

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

Table 2 The relationship of anti-diabetic drugs rules. HbA1c 6.5–7.5

HbA1c 7.6–9.0

First treatment

MET, DPP4, TZD, AGI

MET+

DPP4 TZD SU Glinide

MET + DPP4+

SU

Second treatment

MET+ TZD+ MET+

MET + DPP4+ MET + TZD+

TZD SU SU

MET + TZD+

SU

MET + DPP4+

TZD

MET + DPP4+

DPP4 TZD Glinide SU DPP4 AGI TZD Glinide SU

HbA1c > 9.0

Table 3 The weights of anti-diabetic drugs rules. Weights

Metformin (MET) DPP4 inhibitor Sulfonylurea (SU) Glinide Thiazolidnedione (TZD) Alpha-glucosidase (AGI)

Hypoglycemia

Gastrointestinal

Renal

Liver

Heart

4 4 2 2 4 4

2 4 4 4 4 2

1 3 2 4 3 4

3 4 2 3 1 4

2 4 4 4 0 4

inference processes for a rule system are described as follows (http://herzberg.ca.sandia.gov/): 1. Forward inference is data-oriented, suitable for solving multiobjectives. 2. Backward inference is goal-oriented, suitable for inference with targets. 3. Forward and backward inference may also be combined. Forward inference (data-oriented reasoning) is adopted in the early stages, and then backward inference (goal-oriented reasoning) is adopted after achieving clear results.

Fig. 6. The workflow of DL Reasoner.

of medicine consultant. They construct anti-diabetic drug rules by SWRL Rules. The following rules are a part of anti-diabetic medication. SWRL was used to describe the relationship of anti-diabetic medicine in our study. Table 2 shows the AACEMG for clinical practice for the management of diabetes mellitus. For example, if the value of patient HbA1c is between 6.5 and 7.5, the first treatment can used MET, DPP4, TZD, and AGI drugs. For the second treatment, doctors are suggested to issue MET and DPP4, TZD and AGI or MET and SU. In our study, medicine consultant information was imported into the system. Table 3 shows weights of medicine consultant in each medicine class. For example, if the weight values of Hypoglycemia test, Gastrointestinal test, Liver test, and Heart test are 3 they will be selected for anti-diabetic medicine elements for rule construction. 3.2. Inference of knowledge Users are able to choose a suitable inference engine. If the body of knowledge is removed, the remaining part is the inference, which is called a shell in recommendation systems. Three major

No choice is superior than another. The choice is based mainly on the need of the user’s system. Description Logical Reasoning, Reasoner performed many studies where different reasoning was used (Lau, Tsui, & Lee, 2009). For example, FaCT, OWLJESSKB, DAMLJESSKB, Racer and Pellet are all constructed around different reasoning for OWL. Reasoner’s major components are Parser, Triples and Rule. First, the Reasoner imports an ontology OWL format. Next, OWL Parser transfers OWL into Triples format. And Triple compartmentalizes {subject, object and narrative}. Rules are some restrictions to focus the ontology on a special domain. Finally, DL Reasoner outputs ontology of Classify OWL. Fig. 6 is an workflow of DL Reasoner in ontology. In our anti-diabetic medicine ontology, instance was not used when making real inferences about rules. Through the inference engine (Pellet), Fig. 7 shows how in this study we used the reasoning of Pellet. The knowledge in ontology was translated into the format that the recommendation system accepts, resulting in system operation. In addition, the inference engine can recognize the conflicting and contradictory knowledge in ontology. Our anti-diabetic medicine rules used the SWRL’s format. The SWRL’s format compilation is easy, but the language cannot be used by with the JESS system. So, the system uses XSL to transfer SWRL to a JESS acceptable format for processing. Fig. 8 shows the workflow transfer SWRL to JESS format.

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

4001

Fig. 7. The reasoning of Pellet.

Fig. 8. The workflow of SWRL transformation.

Fig. 9 is the format transformation workflow. First the ontology was transformed into JESS using Pellet. Second the SWRL rules were transferred from JESS using XSLT. 4. Experiment and discussion The experimental data was collected from the AACEMG. The ingredients had 15 classed, including 51 drugs, 96 rules, 6 variables, and 20 patients test data. 4.1. Construct the anti-diabetic drugs system Our study used Protégé to build medicine ontology, which was previously shown with hierarchical relationship structure. Fig. 10 shows the Protégé OWL ontology editor, the ‘‘class’’ presents the hierarchical relationship of patient class and anti-diabetic drug class. The root node of the ontology is ‘‘owl: Thing’’ The child nodes of root node are patient testing and anti-diabetic drug which builds the individual sub-categories. The sub-categories include natural attributes, sub-class, type of dispensing sub-class and side effects sub-class. The system returns recommendations for anti-diabetic drugs including drugs names, ingredients and side effects. In OWL platform, the SWRL rules can be edited in Protégé by selecting the ‘‘SWRLTab’’ plug-in. The SWRL was an XML-based growing out of a framework for building rules on top of OWL ontology. In engine rule, our study used JESS to embed with the Protégé platform to execute rule inference. The JESS software included three components: a rule base, fact base, and execution engine (Mei & Bontas, 2004). Fig. 11 shows the SWRL operation interface, in which the two rectangles drawn using dashed lines comprise the SWRL rule editor and the JESS run-time interface. The rule editor

Fig. 9. The workflow of format transform.

enables users to enter rules as AACEMG for Clinical Practice for The Management of Diabetes Mellitus text. Several our rules were written as shown in the editor. The JESS runtime interface provides triggers for invoking the rule engine. This study has developed six SWRL rules for anti-diabetic drugs relationships between individuals. The individual rules are utilized as detailed below: 1. Rule 1: If the HbA1c level range is between 6.5 and 7.5, the Renal insufficiency test is false, the Gastrointestinal test is false, the Heart test is false, the Hypoglycemia test is false and the Liver test is false; then the DPP4 inhibitor class, Thiazolidinedione, Sulfonylureas, Meglitinide, Biguanides and Alphaglucosidase (AGI) class may be selected.

4002

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

Fig. 10. The editor of Protégé OWL ontology.

Fig. 11. The screenshot of editing SWRL rules and JESS connections.

Rule-1: Body_Diabetes_testing (?bodytest) has_bodytest (?bodytest,?occurrenceitem) A1C_6.5–7.5(?occurrenceitem) use_drugs (?occurrenceitem,?drugs) DPP4_of_Scientific_name (?drugs) Biguanides_of_Scientific_name (?drugs) Meglitinide_of_Scientific_name (?drugs) Sulfonylureas_of_Scientific_name (?drugs)

^ ^ ^ ? ^ ^ ^ ^

Thiazolidinedione_of_Scientific_name (?drugs) AGl_of_Scientific_name (?drugs)

^

2. Rule 2: If the HbA1c level range is between 6.5 and 7.5, and the Liver test is true; then the DPP4 inhibitor class and Alphaglucosidase (AGI) class may be selected. Rule-2: Body_Diabetes_testing (?bodytest) has_bodytest (?bodytest,?occurrenceitem)

^ ^

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

A1C_6.5–7.5(?occurrenceitem) Liver_failure (?occurrenceitem) use_drugs (?occurrenceitem,?drugs) DPP4_of_Scientific_name (?drugs) AGI_of_Scientific_name (?drugs)

^ ^ ? ^

3. Rule 3: If the HbA1c level range is 6.5–7.5, the Renal insufficiency test is true, the Heart test is true, the Hypoglycemia test is true and the Liver test is true; then the Sulfonylureas class and Alpha-glucosidase (AGI) class may be selected. Rule-3: Body_Diabetes_testing (?bodytest) has_bodytest (?bodytest,?occurrenceitem) A1C_6.5–7.5(?occurrenceitem) Renal_insufficiency_test (?occurrenceitem) hypoglycemia_failure (?occurrenceitem) Heart_failure (?occurrenceitem) use_drugs (?occurrenceitem,?drugs) AGl_of_Scientific_name (?drugs) Sulfonylureas_of_Scientific_name (?drugs)

^ ^ ^ ^ ^ ^ ? ^

4. Rule 4: If the HbA1c level range is between 7.5 and 9.0, and the other test are not selected; then the Biguanides class with DPP4 inhibitor class mixed together may be selected. Rule-4: Body_Diabetes_testing (?bodytest) has_bodytest (?bodytest,?occurrenceitem) A1C_7.5–9.0(?occurrenceitem) mix_drugs (?occurrenceitem,?drugs) DPP4_of_Scientific_name (?drugs) Biguanides_of_Scientific_name (?drugs).

^ ^ ^ ? ^

5. Rule 5: If the HbA1c level range is 7.5–9.0 for the second time in treatment, and the other test are not selected; then Biguanides class with DPP4 inhibitor class, Thiazolidinedione class mixed together may be selected. Rule-5: Body_Diabetes_testing (?bodytest) has_bodytest (?bodytest,?occurrenceitem) A1C_7.5–9.0(?occurrenceitem) mix_drugs (?occurrenceitem,?drugs) DPP4_of_Scientific_name (?drugs) Biguanides_of_Scientific_name (?drugs) Thiazolidinedione_of_Scientific_name (?drugs).

^ ^ ^ ? ^ ^

6. Rule-6 says that when the Thiazolidinedione class is used, it says what needs to be monitored and what the side effects are and the contraindications. Rule-6: TZD_of_Scientific_name (?drug) has_TZD_of_Monitoring (?drug,? Monitoring) TZD_of_Monitoring (?Monitoring) has_TZD_of_Possible_Adverse_Effects (?Monitoring, ?PossibleAdverseEffects) TZD_of_Possible_Adverse_Effects (?PossibleAdverseEffects) has_TZD_of_Primary_Mechanism (?PossibleAdverseEffects,? Primary_Mechanism) TZD_of_Primary_Mechanism (?Primary_Mechanism)

^ ^ ^ ^ ^ ^ ?

4003

has_Medicine_Information (?drug,? Primary_Mechanism). 4.2. Recommend of the anti-diabetic drugs First, we will use ‘‘Pellet’’ to find clashes and contradictions in diabetic ontology in this study. Second, the ontology knowledge required importing medicinal information to a JESS engine, because ontology is unable to compute mathematical requirement rules. Fig. 12 shows that OWL and SWRL are integrated and are sent to JESS for reasoning when SWRL has selected 4 rules. The OWL2JESS find 88 classes and 23 instances. In Fig. 13, the patient has HbA1c rate of 6.8% in a range of 6.5%–7.5%, the renal insufficiency testing and the liver are abnormal, therefore anti-diabetic drugs of Meglitinide class were suggested. In this case two drugs were suggested: Repaglinide and Nateglinide. NovoNorm is the brand name of Repaglinide. There are three kinds of dosages: 0.5 mg, 1 mg, and 2 mg. Starlix is the brand name of Nateglinide. There are two kinds of dosage: 60 mg and 120 mg. Fig. 14 shows fasting blood sugar within 2 weeks and HbA1c during 3 months needs to be monitored in anti-diabetic drugs recommendation results. The contraindications included Type 1 diabetes, diabetes with pancreas failure, and severe diseases. The instances from the inferential results satisfied the endocrinologist’s expectation. The anti-diabetic medication recommendation system used rules to build user interface shown as following: The test data input the system has six steps: Step 1: Input HbA1c value of between 6.5% and over 9.0% include 6.5%–7.5%, 7.6%–9.0%, and over 9.0%. The system will compute HbA1c test of value that belongs to parts. Step 2: The liver function test has Normal, Abnormal and Not Available values. If liver function is the normal value or not available value, the system cannot run. If the patients obtain a normal value or not available values, then the patients are normal. Step 3: The renal function test has Normal, Abnormal and Not Available values. The renal function is the normal value or not available value the system cannot run. If the patients obtain a normal value or not available values, then the patients are normal. Step 4: Gastrointestinal dysfunction has Yes or No values. If the gastrointestinal dysfunction is Yes, it is a malformed test, so system can run. Step 5: Heart failure has Yes or No values. If the heart failure is ‘‘Yes’’, it is a malformed test, so system can run. Step 6: Hypoglycemia has Yes or No values. If the hypoglycemia is ‘‘Yes’’, it is a malformed test, so system can run. After six parameters are extracted from users, the system can derive the recommend drugs. Fig. 15 shows the user interface. User inputs HbA1c value between 6.5% and over 9.0%, liver function, renal function, gastrointestinal dysfunction, heart failure and hypoglycemia, and press ‘‘Run’’. Then the system will output recommend drugs and the description of the medicine and restrictions. The description includes component, dose and medication information. The system returns anti-diabetic medication is that suitable for the patient. If the interdiction items are increased, the suggestion

4004

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

Fig. 12. Convert OWL and SWRL to JESS.

Fig. 13. The anti-diabetic drugs of Meglitinide class were suggested.

Fig. 14. The interdiction of Meglitinide class were suggested.

of anti-diabetic medication decreases. Because the system considers more interdiction items, the system is more reasonable resource for patient usage to obtain medication suggestions. Fig. 16

shows the evaluation of how the system returns medication. The Number 1 is the HbA1c test; the system return has 51 anti-diabetic medications that are suitable for patients in that Heart test class.

4005

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

Fig. 15. The user interface.

Table 5 The data of twenty patients.

Fig. 16. The evaluation of system return medication.

The Number 2 is HbA1c test and Heart test; the system returns 21 anti-diabetic medications suitable for patient in that Heart test class. The Number 5 is HbA1c test, Heart test, Liver test, Gastrointestinal test, and Renal test; the system returns 3 anti-diabetic medications suitable for patient in that Heart test class.

No.1 No.2 No.3 No.4 No.5 No.6 No.7 No.8 No.9 No.10 No.11 No.12 No.13 No.14 No.15 No.16 No.17 No.18 No.19 No.20

HbA1c

Hypoglycemia

Gastrointestinal

Renal

Liver

Heart

6.8 6.9 7.6 9 9.6 8 6.7 6.5 7.6 7.9 8.7 7.3 6.9 9 9.4 7 9.6 8 8.3 8.5

0 1 1 1 1 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0

0 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 0 1

0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 1 1 0

0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 0 1 0 0

0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 0

4.3. The evaluation of anti-diabetic drugs system For Anti-Diabetic Drugs Recommend System, system accuracy is very important (Qin & Atluri, 2009). We used the drug system recommendations and let a doctor evaluate the precision of our system. The system performed 20 patient data tests to check precision. In Table 4, True Positive Rate (TP) represents the doctor agree on the recommended drugs. False Negative Rate (FN) represents the doctor disagree on the recommended drugs. The precision rate is determined by dividing TP to that the sum of TP and FN, shown by the following formula (1).

Table 4 The estimation of anti-diabetic drugs system. Parameter

Definition

True positive rate (TP) False negative rate (FN)

The system recommends and the doctor agree The system recommends but the doctor does not agree

TP TP þ FN Recommend Drugs Rate

Precision ¼

¼

ð1Þ

Tatal number of ayatem recommend drugs  100% ð2Þ Total number of recommend drugs of doctor agree

Formulas (2) is mainly used to estimate the effectiveness of an AntiDiabetic Drugs Recommend System. The experiments tested recommend drugs rates and patient data input into the system. The system was able to perform at a precision rate of 100%. Table 5 shows 20 patients of data. If test value is 1, then this test value is occurrence. Table 6 shows the total Anti-Diabetic Drugs System parameters in our system. If the recommendation does not meet doctor requirements, we will revise our rules. The results of this study are good because the system can recommend drugs that meet doctor requirements.

4006

R.-C. Chen et al. / Expert Systems with Applications 39 (2012) 3995–4006

Table 6 The precision of recommending anti-diabetic drugs for twenty patients which evaluated by doctors. Person Precision Person Precision Person Precision Person Precision

No. 1 100% No. 6 100% No. 11 100% No. 16 100%

No. 2 100% No. 7 100% No. 12 100% No. 17 100%

No. 3 100% No. 8 100% No. 13 100% No. 18 100%

No. 4 100% No. 9 100% No. 14 100% No. 19 100%

No. 5 100% No. 10 100% No. 15 100% No. 20 100%

5. Conclusions and future works There are many anti-diabetic pharmaceutical options for doctors to prescribe, especially as time progresses. First, our study used Protégé to build the interrelated anti-diabetic drugs knowledge and patient ontology knowledge. Next, SWRL was used to build the anti-diabetic drugs association rules, and XSLT was used to transform SWRL to a JESS acceptable format. Finally, The inferences of the system is made by JESS, and it carried out the diabetes mellitus medication recommendation detection about instances of monitoring the disease, disease symptoms and side effects. In this system, medicine consultant information was imported into the anti-diabetic drug knowledge system. The system used ‘‘the American association of clinical endocrinologists medical guidelines for clinical practice for the management of diabetes mellitus’’ of HbA1c and ‘‘medicine consultant’’ of Weights. The system will improve a selected candidate for anti-diabetic medicine elements for system construction. In future study, we will strengthen patient ontology and to test more patient data for our system. In other side, we will calculate how much grams the medicine need to be selected for patients. Acknowledgment The authors thank the research support from National Science Council, Taiwan, with number: NSC 98–2221-E-324–031. References Andreasen, T., & Bulskov, H. (2009). Conceptual querying through ontologies. Fuzzy Sets and Systems, 160(15), 2159–2172. Argüello, M., & Des, J. (2007). Clinical practice guidelines: A case study of combining OWL-S, OWL, and SWRL. Applications and Innovations in Intelligent Systems XV, 19–32. Baorto, D., Li, L., & Cimino, J. J. (2009). Practical experience with the maintenance and auditing of a large medical ontology. Journal of Biomedical Informatics, 42(3), 494–503. Bobillo, F., Delgado, M., Gómez-Romero, J., & López, E. (2009). A semantic fuzzy expert system for a fuzzy balanced scorecard. Expert Systems with Applications, 36(1), 423–433. Chen, R. C., Liang, J. Y., & Pan, R. H. (2008). Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency. Expert Systems with Applications, 34(1), 488–501. Chi, Y. L. (2009). Ontology-based curriculum content sequencing system with semantic rules. Expert Systems with Applications, 36(4), 7838–7847.

Choi, N., Song, I. Y., & Han, H. (2006). A survey on ontology mapping. ACM SIGMOD Record, 35(3), 34–41. Coronato, A., Esposito, M., & Pietro, G. D. (2009). A multimodal semantic location service for intelligent environments: An application for smart hospitals. Personal and Ubiquitous Computing, 13(7), 527–538. Grosof, B. (2004). Representing e-commerce rules via situated courteous logicprograms in RuleML. Electronic Commerce Research and Applications, 3(1), 2–20. Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220. Guo, W. Y. (2008). Reasoning with semantic web technologies in ubiquitous computing environment. Journal of Software, 3(8), 27–33. Huang, T.Y. (2003). An Ontology Approach to Modeling the Drugs Guideline for Dyslipidemia Drugs of Bureau of National Health Insurance. Master Thesis, Graduate Institute of Medical Informatics of Taipei Medical University. Jovic, A., Prcela, M., & Gamberger, D. (2007). Ontologies in medical knowledge representation. Information Technology Interfaces, 535–540. Lau, A., Tsui, E., & Lee, W. B. (2009). An ontology-based similarity measurement for problem-based case reasoning. Expert Systems with Applications, 36(3), 6574–6579. Lee, I.J. (2009). A Study on Medical Adverse Events Classification Using Neural Network and Probability Model. Master Thesis, Department of Information Management of Chaoyang University of Technology. Mabotuwana, T., & Warren, J. (2009). An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension. Artificial Intelligence in Medicine, 47(2), 87–103. Mei, J. & Bontas, E.P. (2004). Reasoning paradigms for OWL ontologies. AGN Informationssysteme, 1–23. Newell Shaw, J. C., & Simon, H. A. (1957). Empirical explorations of the logic theory machine: a case study in heuristic. AFIPS Joint Computer Conferences, 218–230. O’Connor, M., Knublauch, H., Tu, S., & Musen, M. (2005). Writing rules for the semantic web using SWRL and JESS. Protégé with rules WS, 1–8. Orchard, R. (2001). Fuzzy reasoning in JESS: The FuzzyJ toolkit and Fuzzy JESS. Proceedings of the Third International Conference on Enterprise Information Systems, 533–542. Prcela, M., Gamberger, D., & Jovic, A. (2008). Semantic web ontology utilization for heart failure expert system design. Stud Health Technol Inform, 1–6. Qin, L., & Atluri, V. (2009). Evaluating the validity of data instances against ontology evolution over the semantic web. Information and Software Technology, 51(1), 83–97. Reformat, M., & Ly, C. (2009). Ontological approach to development of computing with words. International Journal of Approximate Reasoning, 50(1), 72–91. Rodbard, H. W., Blonde, L., Braithwaite, S. S., Brett, E. M., Cobin, R. H., Handelsman, Y., et al. (2007). American association of clinical endocrinologists medical guidelines for clinical practice for the management of diabetes mellitus. American Association of Clinical Endocrinologists, 13(1), 1–68. Rubin, D. L. (2008). Creating and curating a terminology for radiology: Ontology modeling and analysis. Journal of Digital Imaging, 21(4), 355–362. Shortliffe & Hance, E. (1974). MYCIN: A rule-based computer program for advising physicians regarding antimicrobial therapy selection. Technical Report, Stanford University Calif. Dep. of Computer Sciences. Shue, L. Y., Chen, C.-W., & Shiue, W. (2009). The development of an ontology-based expert system for corporate financial rating. Expert Systems with Applications, 36(2), 2130–2142. Snae, C., & Brueckner, M. (2009). Personal health assistance service expert system (PHASES). International Journal of Biological and Medical, 157–160. Temal, L., Dojat, M., Kassel, G., & Gibaud, B. (2008). Towards an ontology for sharing medical images and regions of interest in neuroimaging. Journal of Biomedical Informatics, 41(5), 766–778. Yang, D., Miao, R., Wu, H., & Zhou, Y. (2009). Product configuration knowledge modeling using ontology web language. Expert Systems with Applications, 36(3), 4399–4411. Yang, D., Miao, R., Wu, H., & Zhou, Y. (2009). Product configuration knowledge modeling using ontology web language. Expert Systems with Applications, 36(3), 4399–4411. Zhang, G., Jia, S., & Wang, Q. (2010). Construct ontology-based enterprise information metadata framework. Journal of Software, 5(3), 312–319.