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The role of the Internet in medical decision making
International Journal of Medical Informatics 47 (1997) 43 – 49
The role of the Internet in medical decision making M.F. Anderson *, H. Moazamipour, D.L. Hudson, M.E. Cohen Uni6ersity of California, VA Medical Center, 2615 East Clinton A6enue, Fresno, CA 93703, USA
Abstract The Internet has dramatically changed the means by which information is obtained. Accurate, up-to-date information is vital to maintain the quality of healthcare, especially as US healthcare delivery changes to a primary care-based system. The availability of this new and potentially vast source of information also affects strategies for medical decision making. In this article, use of online information in our medical center is discussed, together with the impact of a locally-developed decision support system. This system first contained components for differential diagnosis as well as computer-assisted instruction. Initially, online searching was limited to Medline literature searches. This component has now been expanded to include important new tools for accessing medical information on the Internet. © 1997 Elsevier Science B.V. Keywords: Medical decision making; Evidenced-based reasoning; Medical imaging
1. Introduction As US healthcare delivery changes to a primary care based system, the need for primary care physicians easily and rapidly to access secondary and tertiary care information becomes vital if the quality of healthcare is to be maintained. The Internet provides access to information available locally, within a network of medical centers, and at centers and agencies throughout the world. Many medical school departments are changing
their teaching and practice to an evidencebased medicine approach which relies on a world-wide access to appropriate knowledge.
* Corresponding author. Tel.: + 1 209 2285358; fax: +1 209 2286903; e-mail: [email protected] 1386-5056/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved. PII S 1 3 8 6 - 5 0 5 6 ( 9 7 ) 0 0 0 7 0 - 1
Fig. 1. Decision support system.
44
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
Fig. 3. Computer-assisted instruction subsystem. Fig. 2. Differential diagnosis.
In addition, physicians are becoming accustomed to new methods of making clinical decisions that use formal rules of evidencebased information that may be obtained through the Internet. This paper summarizes some of the sources of Internet information which are used at our medical center and in our medical education program, along with locally developed decision support software. Details are given on the adaptation of this software to incorporate new sources of information from the Internet.
2. Theory In 1989, we began a project to develop a medical decision support system consisting of components for computer-assisted instruction and differential diagnosis [1] at University of California, San Francisco, Fresno, Medical Education Program. The major components in the original design are shown in Fig. 1. Subsystems of components in varying shades of grey are illustrated in subsequent figures. Fig. 2 shows the differential diagnosis subsystem, Fig. 3 the computer-assisted instruction subsystem, and Fig. 4 the consultation subsystem. Dotted borders indicate linkages to Internet components. The system is icon-
driven and PC-based with some algorithms on the PC and some on the local SUN SPARCserver. Access to facilities on the local server and the Internet is seamless and may not even be apparent to the user.
3. Hybrid system components The differential diagnosis subsystem contains three components: the hybrid system, Reconsider, a system developed by M.S. Blois et al. at UCSF, and image analysis. The hybrid system itself includes four components: a knowledge-based system, a neural network model, time series analysis, and medical image analysis.
3.1. Knowledge-based system The oldest component is the knowledgebased system, which has been updated to
Fig. 4. Consultation.
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
included different approaches depending on the aspects of uncertainty which are present [2]: crisp implementation; partial substantiation of antecedents; weighted antecedents and partial substantiation of rules. The crisp implementation allows rule antecedents in three forms: conjunctions (AND), disjunctions (OR), and a specified number in a list (COUNT) followed by an integer indicating how many in the list must be substantiated. The inclusion of these three logical constructs permits the types of reasoning most often identified in the human thought process. When using the crisp form of the expert system, the presence of all symptoms and results of all tests are considered to be all or nothing, with no degrees of severity indicated. The only uncertainty included is the presence of certainty factors associated with each rule which indicate the certainty that the substantiation of the rule points to the presence of the relevant condition. It is seldom the case that it is acceptable to ignore degrees of presence of symptoms as nuances in the data are lost. Partial substantiation of antecedents can be accomplished in several ways. The most straightforward changes the user interaction with the system so that the user responds with a degree of presence, a number between 0 and 10. Alternately, the user can enter a value directly, for example a test result, and then pre-defined membership functions are invoked to determine a degree of presence. Weighted antecedents permit the knowledge base designer to attach degrees of relative importance to the contributing factors.
3.2. Neural network model Variables are incorporated into a neural network model which utilizes a learning algorithm developed by the authors [3]. It features a non-statistical approach based on
45
supervised learning. Weights are adjusted using orthogonal functions. The basis of the technique is generalized vector spaces permitting the development of multidimensional nonlinear decision surfaces. This learning algorithm has a number of advantages over more traditional statistically-based back propagation networks: dependent features are easily handled, missing information can be accommodated, and convergence of the system is assured. In addition, the output can be used to determine which parameters were important in the decision process. In many applications, a number of the input nodes will represent dependent information. Although statistically-based systems can accommodate dependent features, great care must be taken in handling them. The model described here is also quite robust in handling missing information. Guaranteed convergence is especially important. Work in the last decade with recursive systems has shown not only that such systems can propagate error, but under some circumstances, the systems will produce chaotic behavior. It can be shown theoretically that the method used here will not result in divergence or chaos.
3.3. Time series analysis In the last decade, chaos theory has become a popular method for approaching the analysis of nonlinear data for which most mathematical models produce intractable solutions. The concept of chaos was first introduced in meteorology. Since then, considerable work has been done in the theoretical aspects of chaos. Applications have abounded, especially in medicine. A particularly active area for the application of chaos theory has been cardiology. Most approaches to chaotic modeling rely on discrete models of continuous problems which are represented by computer algorithms. Due to the
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
46
nature of chaotic models, both the discretization and the computer simulation can lead to propagation of errors which may overtake the actual solution. In the approach described here, a continuous model is developed based on a conjectured solution to the logistic equation. An example of a chaotic equation, the logistic equation is: an = Aan − 1(1− an − 1)
25 A 54
(1)
An exact solution exists in the chaotic region only at A= 4. We have developed a conjectured solution which is valid for any value of A in the important range 2 5A 54 [4]. This theoretical work lead to two practical measures for quantifying variability in time series data sets. The first is a graphical representation of the second-order difference: (an + 2 − an + 1) vs. (an + 1 − an ) which produces plots centered around the origin which have been shown to be useful in modeling biological systems, such as hemodynamics and heart rate variations. The difference approach appears to give a more robust picture of the problem and fits well within our theoretical results of the continuous logistic equation. The second is the Central Tendency Measure
t−2
n = % d(di ) i=1
n,
4. Material and methods
4.1. Enhancements from Internet access
(t− 2)
where d(di ) =
The main menu choices include VIEW which allows the user to view images which have already been digitized and stored either through the use of a video camera or from the video disk of the American College of Radiology Teaching File. This component has now been augmented by online Internet access to additional files discussed later; LIVE which allows the user to digitize and store new images either through the use of a video camera connected to an AT&T Targa Board or from a VCR; ENHANCE which includes a number of options: Map, Smooth, Equalize Histogram; Strectch Histogram: Sharpen edges, Scale, B/W; FILTER: utilizes a new class of digital filters based on an orthogonal function developed by Cohen which are applicable in both one and two dimensions. We have a adapted our original concept the our medical decision support system to incorporate medical information from the Internet from sources which are deemed to be reliable and as up-to-date as possible.
1 if [(a i + 2 − ai + 1)2 + (ai + 1 − ai )2]0.5 Br 0 otherwise (2)
which summarizes information contained in the second-order difference graph. The node value n will thus be a number between O and 1, inclusive, and can be added to the neural network model.
3.4. Medical image analysis The imaging component is menu-driven [5].
4.1.1. Discipline-specific databases A number of online resources are being used in the Obstetrics/Gynecology Department at University of California, San Francisco (UCSF: Cochrane Pregnancy and Childbirth Database on computer; Epi Info, version 6, a public-domain software package for microcomputers; 24-h access to free MEDLINE searches via the Internet; the ACOG Resource Center; ACOGNET; and several paperback texts on critical appraisal [6]. 4.1.2. Special interest groups Special mailing lists can be established on the Internet for communication within a
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
group with a common interest. We have established such a group for pediatric cardiology and surgery, called PediHeart©. The leader in the establishment of this group is Adam Birek, M.D., a pediatric anesthesiologist, Valley Children’s Hospital, Fresno, California. The mailing list for this group resides on the USCF-Fresno computer and provides world-wide communications for the group, currently over 700 subscribers. UK-GP, based in the UK, but with international participants, provides another example of Internet interaction between physicians [7].
4.1.3. Library information In addition to literature searching, online information is vital. The best solution would be to have articles available directly online in the form of a digital library. Planning, design, and implementation of a ‘free-standing’ Digital Library of the Health Sciences (Galen H), focusing on the innovative application of technology have begun. An important component of this effort is the Red Sage project, an experimental project founded by UCSF, AT&T Bell Laboratories and Springer-Verlag. The goal is to provide electronic access to high impact clinical journals. Currently this service is available on the UCSF campus but access at remote sites requires high speed direct transmission lines as the data is in image format.
47
in medical decision making. Many physicians, especially in primary care, find access to evidence-based medical information especially valuable. These databases include the Cochrane collaboration, Bandolier (a monthly journal from Oxford) and many others.
4.1.5. Imaging databases Another active area of database creation is radiology. These databases contain archived images representing healthy and diseased conditions. There are numerous examples, of which one is a system called CHORUS (Collaborative Hypertext of Radiology) was developed to facilitate collaboration among physicians. It consists of a computer-based radiology handbook developed and published electronically via the World Wide Web which allows physicians without computer expertise to read documents, contribute knowledge, and critically review the handbook’s content using a simple, graphical interface from any type of computer [8]. Distance Learning Several masters degree courses are becoming available using the Internet. One of the first was started at the University of Derby. Direct instruction to classes either on a local network or in rural areas is also feasible.
5. Local decision support systems
4.1.4. Online medical databases In addition to MEDLINE, a growing number of databases exist on the Internet which can be freely accessed, including chemical abstracts, conference papers, dissertation abstracts, federal research in progress, pharmaceutical abstracts, science citation index, and social science citation index. The NLM maintains a number of bibliographic databases. Many other World Wide Web services are becoming important sources of information
The hybrid system discussed above has been expanded in two directions [9]. First, the system now allows the incorporation of information extracted from medical signal data into the reasoning process. The analysis is based on a continuous chaotic model developed by the authors, and has been successfully applied to the analysis of hemodynamic data in an animal model and the analysis of 24-h R-R interval data in patients with con-
48
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
gestive heart failure. Current work is underway in the analysis of electroencephalogram data from patients with Alzheimer’s disease. The second direction of expansion includes other resources available locally or through Internet access. This component has been used to enhance the ability of the decision support system to deal with image data. Signal analysis and image data supply important parameters to the medical decision making process and must be incorporated into comprehensive decision aids.
6. Results The following is a consultation as it would appear to the user, invoking both the rulebased system, the neural network model, and chaotic analysis of signal data. The neural network model is imbedded in the rule-based model and is triggered by the invocation of certain rules. In addition to this direct connection, the neural network model is used to obtain weighting factors and thresholds for many of the rules in the knowledge-based program. Patient name: TC Age: 67 Chief complaint: chest pain Hx of coronary problems y/n? y Hx CAD y/n? y Recent ETT data (Neural Network) Max ST depression in mm: 2.0 HR at beginning of ETT: 57 HR at end of ETT: 106 BP at beginning of ETT: 146 BP at end of ETT: 148 Hx of CHP y/n? y Computer file of holter data available y/n? y Enter file name: TCholter (Chaotic Analysis) List current symptoms, followed by a de-
gree of severity between 0 (n) and 10 (y): excruciating pain 9 (KnowledgeBased System) *Pain unremitting? 8 Patient should be admitted to hospital (inpatient). ETT data indicate 3 6essel CAD Holter data indicates significant chaos in R-R inter6als related to CHF Do you want an explanation of these conclusions? y The following rules from the knowledgebased chest pain analysis were substantiated (in re6erse order): IF pain excruciating AND pain unremitting THEN Patient should be admitted IF Chest pain THEN Proceed to other symptom analysis The neural network modelfor ETT concluded 3-vessel disease based on change in HR during test, change in BP during test, double produce of HR and BP, and maximum ST depression. Chaotic analysis of R-R intervals from the 2-h Holter tape indicates a central tendency of 14%, indicating a very high level of variability.
7. Discussion During the intervening 7 years since our group began development of a comprehensive medical decision support system, many things have changed in the method by which information, including medical information is delivered. No doubt the greatest advance is the use of the Internet for instantaneous world-wide communication. Because of the modular nature of the original system, it was relatively easy to adapt the system to include new sources of information without major changes to the structure. Hopefully this modularity will continue to provide a good frame-
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
work as the sources and diversity of information change in the future. While the Internet has been a boon to collaborative research and coordination of activities among universities and other research centers, it has also brought forward a number of new issues in the analysis of medical information. The foremost of these is the problem of validation of the accuracy and applicability of new information. This is a problem which will be exacerbated in the future due to the continuing information explosion.
8. Conclusion The Internet has become an important source of information for physicians seeking immediate data for the management of patients and for those developing decision making methodologies and guidelines for clinical practice. Medical decision support systems need to take advantage of all available information, including expert input, database information, and nontextual information such as medical time series and image information. In the hybrid approach described here, both the expert input and the database information can be incorporated by using the combination of the rulebased system and the neural network model. The overall system is designed so that new databases can easily be incorporated, whether they are local or accessible using the Internet. The approach has been evaluated in several medical applications in addition to the chest pain illustration given here, including prognosis in melanoma, diagnosis and treatment of lung cancer, and analysis of drug effects on the hepatic system.
49
Acknowledgements This work was supported in part by a grant from University of California Valley Medical Education Foundation. References [1] D.L. Hudson, M.E. Cohen, M.F. Anderson, Computer-assisted differential diagnosis and management, Hawaii Int. Conf. on System Sciences, Vol. 24, IEEE Computer Society Press, 1991, pp. 218 –226 [2] M.E. Cohen, D.L. Hudson, Integration of neural network techniques with approximate reasoning techniques in knowledge-based systems, in: A. Kandel, G. Langholz (Eds.), Hybrid Architectures for Intelligent Systems, Vol. 72, CRC Press, 1992, pp. 72 – 85. [3] D.L. Hudson, M.E. Cohen, M.F. Anderson, Use of neural network techniques in a medical expert system, Int. J. Intell. Syst. 6 (2) (1991) 213 – 223. [4] M.E. Cohen, D.L. Hudson, M.F. Anderson, P.C. Deedwania, A conjecture to the solution of the continuous logistic equation, Int. J. Uncertainty, Fuzziness Knowledge-Based Systems 2 (4) (1994) 445 – 461. [5] H. Moazamipour, D.L. Hudson, M.E. Cohen, M.F. Anderson, An image processing system for computer-assisted medical instruction, ISMM Mini/Microcomputers in Med. and Health Care (1991) 37 – 40. [6] D.A. Grimes, Introducing evidence-based medicine into a department of obstetrics and gynecology, Obstet. Gynecol. 86 (3) (1995) 451 – 457. [7] H. Moszamipour, D.L. Hudson, M.E. Cohen, M.F. Anderson, Use of the Internet to assist medical decision making, Proc. ISCA 11 (1996) 272 – 275. [8] R. Martinex, W. Chimiak, J. Kim, Y. Alsafadi, The rural and global medical informatics consortium and network for radiology services, Comput. Biol. Med. 25 (2) (1995) 85 – 106. [9] D.L. Hudson, M.E. Cohen, M.F. Anderson, P.C. Deedwania, A Hybrid Decision Support System Incorporating Nontextual Information and Internet Access, International Society for Computers and their Applications, 1997, (in press).
The role of the Internet in medical decision making M.F. Anderson *, H. Moazamipour, D.L. Hudson, M.E. Cohen Uni6ersity of California, VA Medical Center, 2615 East Clinton A6enue, Fresno, CA 93703, USA
Abstract The Internet has dramatically changed the means by which information is obtained. Accurate, up-to-date information is vital to maintain the quality of healthcare, especially as US healthcare delivery changes to a primary care-based system. The availability of this new and potentially vast source of information also affects strategies for medical decision making. In this article, use of online information in our medical center is discussed, together with the impact of a locally-developed decision support system. This system first contained components for differential diagnosis as well as computer-assisted instruction. Initially, online searching was limited to Medline literature searches. This component has now been expanded to include important new tools for accessing medical information on the Internet. © 1997 Elsevier Science B.V. Keywords: Medical decision making; Evidenced-based reasoning; Medical imaging
1. Introduction As US healthcare delivery changes to a primary care based system, the need for primary care physicians easily and rapidly to access secondary and tertiary care information becomes vital if the quality of healthcare is to be maintained. The Internet provides access to information available locally, within a network of medical centers, and at centers and agencies throughout the world. Many medical school departments are changing
their teaching and practice to an evidencebased medicine approach which relies on a world-wide access to appropriate knowledge.
* Corresponding author. Tel.: + 1 209 2285358; fax: +1 209 2286903; e-mail: [email protected] 1386-5056/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved. PII S 1 3 8 6 - 5 0 5 6 ( 9 7 ) 0 0 0 7 0 - 1
Fig. 1. Decision support system.
44
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
Fig. 3. Computer-assisted instruction subsystem. Fig. 2. Differential diagnosis.
In addition, physicians are becoming accustomed to new methods of making clinical decisions that use formal rules of evidencebased information that may be obtained through the Internet. This paper summarizes some of the sources of Internet information which are used at our medical center and in our medical education program, along with locally developed decision support software. Details are given on the adaptation of this software to incorporate new sources of information from the Internet.
2. Theory In 1989, we began a project to develop a medical decision support system consisting of components for computer-assisted instruction and differential diagnosis [1] at University of California, San Francisco, Fresno, Medical Education Program. The major components in the original design are shown in Fig. 1. Subsystems of components in varying shades of grey are illustrated in subsequent figures. Fig. 2 shows the differential diagnosis subsystem, Fig. 3 the computer-assisted instruction subsystem, and Fig. 4 the consultation subsystem. Dotted borders indicate linkages to Internet components. The system is icon-
driven and PC-based with some algorithms on the PC and some on the local SUN SPARCserver. Access to facilities on the local server and the Internet is seamless and may not even be apparent to the user.
3. Hybrid system components The differential diagnosis subsystem contains three components: the hybrid system, Reconsider, a system developed by M.S. Blois et al. at UCSF, and image analysis. The hybrid system itself includes four components: a knowledge-based system, a neural network model, time series analysis, and medical image analysis.
3.1. Knowledge-based system The oldest component is the knowledgebased system, which has been updated to
Fig. 4. Consultation.
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
included different approaches depending on the aspects of uncertainty which are present [2]: crisp implementation; partial substantiation of antecedents; weighted antecedents and partial substantiation of rules. The crisp implementation allows rule antecedents in three forms: conjunctions (AND), disjunctions (OR), and a specified number in a list (COUNT) followed by an integer indicating how many in the list must be substantiated. The inclusion of these three logical constructs permits the types of reasoning most often identified in the human thought process. When using the crisp form of the expert system, the presence of all symptoms and results of all tests are considered to be all or nothing, with no degrees of severity indicated. The only uncertainty included is the presence of certainty factors associated with each rule which indicate the certainty that the substantiation of the rule points to the presence of the relevant condition. It is seldom the case that it is acceptable to ignore degrees of presence of symptoms as nuances in the data are lost. Partial substantiation of antecedents can be accomplished in several ways. The most straightforward changes the user interaction with the system so that the user responds with a degree of presence, a number between 0 and 10. Alternately, the user can enter a value directly, for example a test result, and then pre-defined membership functions are invoked to determine a degree of presence. Weighted antecedents permit the knowledge base designer to attach degrees of relative importance to the contributing factors.
3.2. Neural network model Variables are incorporated into a neural network model which utilizes a learning algorithm developed by the authors [3]. It features a non-statistical approach based on
45
supervised learning. Weights are adjusted using orthogonal functions. The basis of the technique is generalized vector spaces permitting the development of multidimensional nonlinear decision surfaces. This learning algorithm has a number of advantages over more traditional statistically-based back propagation networks: dependent features are easily handled, missing information can be accommodated, and convergence of the system is assured. In addition, the output can be used to determine which parameters were important in the decision process. In many applications, a number of the input nodes will represent dependent information. Although statistically-based systems can accommodate dependent features, great care must be taken in handling them. The model described here is also quite robust in handling missing information. Guaranteed convergence is especially important. Work in the last decade with recursive systems has shown not only that such systems can propagate error, but under some circumstances, the systems will produce chaotic behavior. It can be shown theoretically that the method used here will not result in divergence or chaos.
3.3. Time series analysis In the last decade, chaos theory has become a popular method for approaching the analysis of nonlinear data for which most mathematical models produce intractable solutions. The concept of chaos was first introduced in meteorology. Since then, considerable work has been done in the theoretical aspects of chaos. Applications have abounded, especially in medicine. A particularly active area for the application of chaos theory has been cardiology. Most approaches to chaotic modeling rely on discrete models of continuous problems which are represented by computer algorithms. Due to the
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
46
nature of chaotic models, both the discretization and the computer simulation can lead to propagation of errors which may overtake the actual solution. In the approach described here, a continuous model is developed based on a conjectured solution to the logistic equation. An example of a chaotic equation, the logistic equation is: an = Aan − 1(1− an − 1)
25 A 54
(1)
An exact solution exists in the chaotic region only at A= 4. We have developed a conjectured solution which is valid for any value of A in the important range 2 5A 54 [4]. This theoretical work lead to two practical measures for quantifying variability in time series data sets. The first is a graphical representation of the second-order difference: (an + 2 − an + 1) vs. (an + 1 − an ) which produces plots centered around the origin which have been shown to be useful in modeling biological systems, such as hemodynamics and heart rate variations. The difference approach appears to give a more robust picture of the problem and fits well within our theoretical results of the continuous logistic equation. The second is the Central Tendency Measure
t−2
n = % d(di ) i=1
n,
4. Material and methods
4.1. Enhancements from Internet access
(t− 2)
where d(di ) =
The main menu choices include VIEW which allows the user to view images which have already been digitized and stored either through the use of a video camera or from the video disk of the American College of Radiology Teaching File. This component has now been augmented by online Internet access to additional files discussed later; LIVE which allows the user to digitize and store new images either through the use of a video camera connected to an AT&T Targa Board or from a VCR; ENHANCE which includes a number of options: Map, Smooth, Equalize Histogram; Strectch Histogram: Sharpen edges, Scale, B/W; FILTER: utilizes a new class of digital filters based on an orthogonal function developed by Cohen which are applicable in both one and two dimensions. We have a adapted our original concept the our medical decision support system to incorporate medical information from the Internet from sources which are deemed to be reliable and as up-to-date as possible.
1 if [(a i + 2 − ai + 1)2 + (ai + 1 − ai )2]0.5 Br 0 otherwise (2)
which summarizes information contained in the second-order difference graph. The node value n will thus be a number between O and 1, inclusive, and can be added to the neural network model.
3.4. Medical image analysis The imaging component is menu-driven [5].
4.1.1. Discipline-specific databases A number of online resources are being used in the Obstetrics/Gynecology Department at University of California, San Francisco (UCSF: Cochrane Pregnancy and Childbirth Database on computer; Epi Info, version 6, a public-domain software package for microcomputers; 24-h access to free MEDLINE searches via the Internet; the ACOG Resource Center; ACOGNET; and several paperback texts on critical appraisal [6]. 4.1.2. Special interest groups Special mailing lists can be established on the Internet for communication within a
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
group with a common interest. We have established such a group for pediatric cardiology and surgery, called PediHeart©. The leader in the establishment of this group is Adam Birek, M.D., a pediatric anesthesiologist, Valley Children’s Hospital, Fresno, California. The mailing list for this group resides on the USCF-Fresno computer and provides world-wide communications for the group, currently over 700 subscribers. UK-GP, based in the UK, but with international participants, provides another example of Internet interaction between physicians [7].
4.1.3. Library information In addition to literature searching, online information is vital. The best solution would be to have articles available directly online in the form of a digital library. Planning, design, and implementation of a ‘free-standing’ Digital Library of the Health Sciences (Galen H), focusing on the innovative application of technology have begun. An important component of this effort is the Red Sage project, an experimental project founded by UCSF, AT&T Bell Laboratories and Springer-Verlag. The goal is to provide electronic access to high impact clinical journals. Currently this service is available on the UCSF campus but access at remote sites requires high speed direct transmission lines as the data is in image format.
47
in medical decision making. Many physicians, especially in primary care, find access to evidence-based medical information especially valuable. These databases include the Cochrane collaboration, Bandolier (a monthly journal from Oxford) and many others.
4.1.5. Imaging databases Another active area of database creation is radiology. These databases contain archived images representing healthy and diseased conditions. There are numerous examples, of which one is a system called CHORUS (Collaborative Hypertext of Radiology) was developed to facilitate collaboration among physicians. It consists of a computer-based radiology handbook developed and published electronically via the World Wide Web which allows physicians without computer expertise to read documents, contribute knowledge, and critically review the handbook’s content using a simple, graphical interface from any type of computer [8]. Distance Learning Several masters degree courses are becoming available using the Internet. One of the first was started at the University of Derby. Direct instruction to classes either on a local network or in rural areas is also feasible.
5. Local decision support systems
4.1.4. Online medical databases In addition to MEDLINE, a growing number of databases exist on the Internet which can be freely accessed, including chemical abstracts, conference papers, dissertation abstracts, federal research in progress, pharmaceutical abstracts, science citation index, and social science citation index. The NLM maintains a number of bibliographic databases. Many other World Wide Web services are becoming important sources of information
The hybrid system discussed above has been expanded in two directions [9]. First, the system now allows the incorporation of information extracted from medical signal data into the reasoning process. The analysis is based on a continuous chaotic model developed by the authors, and has been successfully applied to the analysis of hemodynamic data in an animal model and the analysis of 24-h R-R interval data in patients with con-
48
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
gestive heart failure. Current work is underway in the analysis of electroencephalogram data from patients with Alzheimer’s disease. The second direction of expansion includes other resources available locally or through Internet access. This component has been used to enhance the ability of the decision support system to deal with image data. Signal analysis and image data supply important parameters to the medical decision making process and must be incorporated into comprehensive decision aids.
6. Results The following is a consultation as it would appear to the user, invoking both the rulebased system, the neural network model, and chaotic analysis of signal data. The neural network model is imbedded in the rule-based model and is triggered by the invocation of certain rules. In addition to this direct connection, the neural network model is used to obtain weighting factors and thresholds for many of the rules in the knowledge-based program. Patient name: TC Age: 67 Chief complaint: chest pain Hx of coronary problems y/n? y Hx CAD y/n? y Recent ETT data (Neural Network) Max ST depression in mm: 2.0 HR at beginning of ETT: 57 HR at end of ETT: 106 BP at beginning of ETT: 146 BP at end of ETT: 148 Hx of CHP y/n? y Computer file of holter data available y/n? y Enter file name: TCholter (Chaotic Analysis) List current symptoms, followed by a de-
gree of severity between 0 (n) and 10 (y): excruciating pain 9 (KnowledgeBased System) *Pain unremitting? 8 Patient should be admitted to hospital (inpatient). ETT data indicate 3 6essel CAD Holter data indicates significant chaos in R-R inter6als related to CHF Do you want an explanation of these conclusions? y The following rules from the knowledgebased chest pain analysis were substantiated (in re6erse order): IF pain excruciating AND pain unremitting THEN Patient should be admitted IF Chest pain THEN Proceed to other symptom analysis The neural network modelfor ETT concluded 3-vessel disease based on change in HR during test, change in BP during test, double produce of HR and BP, and maximum ST depression. Chaotic analysis of R-R intervals from the 2-h Holter tape indicates a central tendency of 14%, indicating a very high level of variability.
7. Discussion During the intervening 7 years since our group began development of a comprehensive medical decision support system, many things have changed in the method by which information, including medical information is delivered. No doubt the greatest advance is the use of the Internet for instantaneous world-wide communication. Because of the modular nature of the original system, it was relatively easy to adapt the system to include new sources of information without major changes to the structure. Hopefully this modularity will continue to provide a good frame-
M.F. Anderson et al. / International Journal of Medical Informatics 47 (1997) 43–49
work as the sources and diversity of information change in the future. While the Internet has been a boon to collaborative research and coordination of activities among universities and other research centers, it has also brought forward a number of new issues in the analysis of medical information. The foremost of these is the problem of validation of the accuracy and applicability of new information. This is a problem which will be exacerbated in the future due to the continuing information explosion.
8. Conclusion The Internet has become an important source of information for physicians seeking immediate data for the management of patients and for those developing decision making methodologies and guidelines for clinical practice. Medical decision support systems need to take advantage of all available information, including expert input, database information, and nontextual information such as medical time series and image information. In the hybrid approach described here, both the expert input and the database information can be incorporated by using the combination of the rulebased system and the neural network model. The overall system is designed so that new databases can easily be incorporated, whether they are local or accessible using the Internet. The approach has been evaluated in several medical applications in addition to the chest pain illustration given here, including prognosis in melanoma, diagnosis and treatment of lung cancer, and analysis of drug effects on the hepatic system.
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Acknowledgements This work was supported in part by a grant from University of California Valley Medical Education Foundation. References [1] D.L. Hudson, M.E. Cohen, M.F. Anderson, Computer-assisted differential diagnosis and management, Hawaii Int. Conf. on System Sciences, Vol. 24, IEEE Computer Society Press, 1991, pp. 218 –226 [2] M.E. Cohen, D.L. Hudson, Integration of neural network techniques with approximate reasoning techniques in knowledge-based systems, in: A. Kandel, G. Langholz (Eds.), Hybrid Architectures for Intelligent Systems, Vol. 72, CRC Press, 1992, pp. 72 – 85. [3] D.L. Hudson, M.E. Cohen, M.F. Anderson, Use of neural network techniques in a medical expert system, Int. J. Intell. Syst. 6 (2) (1991) 213 – 223. [4] M.E. Cohen, D.L. Hudson, M.F. Anderson, P.C. Deedwania, A conjecture to the solution of the continuous logistic equation, Int. J. Uncertainty, Fuzziness Knowledge-Based Systems 2 (4) (1994) 445 – 461. [5] H. Moazamipour, D.L. Hudson, M.E. Cohen, M.F. Anderson, An image processing system for computer-assisted medical instruction, ISMM Mini/Microcomputers in Med. and Health Care (1991) 37 – 40. [6] D.A. Grimes, Introducing evidence-based medicine into a department of obstetrics and gynecology, Obstet. Gynecol. 86 (3) (1995) 451 – 457. [7] H. Moszamipour, D.L. Hudson, M.E. Cohen, M.F. Anderson, Use of the Internet to assist medical decision making, Proc. ISCA 11 (1996) 272 – 275. [8] R. Martinex, W. Chimiak, J. Kim, Y. Alsafadi, The rural and global medical informatics consortium and network for radiology services, Comput. Biol. Med. 25 (2) (1995) 85 – 106. [9] D.L. Hudson, M.E. Cohen, M.F. Anderson, P.C. Deedwania, A Hybrid Decision Support System Incorporating Nontextual Information and Internet Access, International Society for Computers and their Applications, 1997, (in press).