"EXPERT SYSTEMS AND OPEN SYSTEMS IN MEDICAL ARTIFICIAL INTELLIGENCE."
 

John H. Frenster, M.D., FACP
Physicians' Educational Series,
Atherton, CA 94027-5446

• Abstract
• Introduction
• Medical Expert Systems
• Medical Open Systems
• The Phases of Clinical Care
• Matrix Cognition
• Mathematical Logic
• References




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Abstract:
Expert systems provide pre-selected rules for decision-making within specialized domains of knowledge, but are limited by the fixed choices and by the date of the expert opinions embodied in the decision rules. Open systems, by contrast, allow user formulation both of the potential decisions as well as of the decision elements leading to each decision, while quantitating the contribution of each element to each potential decision. Open systems can then calculate the best decision, the analysis of variance, the logical inconsistency of selections, and the performance consistency of ratings by utilizing the mathematical techniques of the analysis of queueing and renewal with matrix cognition.

Medical Expert Systems:
Medical expert systems have evolved to provide physicians with both structured questions and structured responses within medical domains of specialized knowledge or experience (1). The structure is embodied in the program on the advice of one or more medical experts, who also suggest the optimal questions to consider, and provide the most accurate conclusions to be drawn from the answers the physician chooses. In software programs, these decision sequences are represented in clauses of the form: "If..., Then..., Else...", with final else having positive value in the closed system of the program (5). Although the physician is free to select any one of the choices offered in each clause, the physician is limited to the choices offered by the expert in writing the program. The program is thus limited by the fixed input from the expert at the particular time of formulation. If the physician has new questions or new data, a medical expert system program will not be able to accomodate the physician. It is for this basic reason that open system programs have been developed to meet the new needs of the user (5), with the contrast being paraphrased as: "Expert Systems are by experts; Open Systems are for experts".

Medical Open Systems:
Open systems in medical artificial intelligence allow physician initiative in formulating both the decision outcomes and the decision elements leading to such outcomes. Each physician user is allowed to formulate 2-10 potential outcomes and 1-10 contributing elements before proceeding with the analysis (3). The physician then compares the decision elements for relative importance in a pairwise response matrix that generates an eigenvalue in a system of matrix cognition (6). The final decision outcome is then calculated from the weighted decision elements, and the response eigenvalue is used to calculate the response variance, the logical inconsistency of the selections, and the performance consistency of the ratings (7) for each user on each program run. The analysis of queueing and renewal within human systems (8) has proven useful in generating both potential decision outcomes and in identifying decision elements in awide variety of clinical programs (3).

The Phases of Clinical Care:
The clinical care of a particular patient often proceeds in distinct phases, such as diagnosis before therapy, or prevention of disease before onset of disease, or rehabilitation of the patient after therapy of the patient (2). The analysis of queueing and renewal within human systems (8) has permited the identification of both decision elements and potential decisions in at least 10 such distinct phases of clinical care (3):
 

Decision Elements and Potential Decisions in Clinical Care.

Phase of Care	Decision Elements	Potential Decisions
Prediction of Disease	Risk Factors Present	Predicted Disease
Prevention of Disease	Motivation of Patient	Preventive Measures
Diagnosis of Disease	Diagnostic Findings	Disease Diagnosis
Staging of Disease	Staging Factors Present	Disease Stage
Therapy of Patient	Pathologic States Present	Therapy Selected
Rehabilitation of Patient	Residual Defects Present	Schedule Selected
Health Status of the Patient	Specific Load Tolerances	Specific Capacities
Counseling of the Patient	Specific PatientConcerns	Specific Advice
Advocacy for the Patient	Specific Dangers to Patient	Specific Defenses
Financing for the Patient	Specific Medical Expenses	Specific Funding

Following the identification of both decision elements and potential decisions in these distinct phases of clinical care, it became possible to develop open systems for decision-making, utilizing a system of matrix cognition (6) for each clinical phase (3), and allowing the teaching of the mathematical approach for physicians in each of these distinct clinical phases (2).

Matrix Cognition:
The development by Saaty of a mathematical analysis of pairwise comparisons of user responses (6) has continued to exert a profound influence on computer applications designed to enhance interactive human cognition (7). The matrix methods developed by Saaty permit a reduction of many human evaluations to a long series of pairwise comparisons, in which the accumulating results are stored for matrix calculation while the user can focus serially on distinguishing only two qualities or two quantities (3). Earlier studies by Miller (9) had demonstrated the limited capacity of all humans to process more than 7 plus or minus 2 separate foci of information at any one time, and the development of matrix cognition with computer storage of user evaluations for later matrix calculation of the response eigenvalues promises to maximize the human capability for two-point discrimination by interaction with such open systems (5).

Mathematical Logic:
John McCarthy has recently written on the importance of viewing artificial intelligence "as a branch of computer science rather than as branch of psychology" (10), but the two fields are rapidly converging, and further benefitting from the introduction of mathematical techniques and logical rigour, which now seem to permit human cognition over an extended time frame and complexity net. Whether "the study of Artificial Intelligence may lead to a mathematical metaepistemology analagous to metamathematics" (10) is problematical, but the development of open systems and their use by inquistive physicians may eventually help our patients most of all.

References:
1. Luger GF, Stubblefield WA, Artificial intelligence and the design of expert systems, Redwood City, CA Benjamin/Cummings Publ. Co. 1989.

2. Frenster JH, Physicians' 1,2,3,4,5: Teaching physicians to think mathematically about each of their patient's problems. Innovations in Medical Education 1987; vol. 12, pages 87-88, Assoc. Am. Med. Colleges, Washington, DC.

3. Frenster JH, Expert systems and open systems within medical decision-making, Clin Research April, 1989 Vol 37. (Abstract).

4. Kant E, Interactive problem solving using task configuration and control. IEEE-Expert 1988; Winter: 36-49.

5. Hewitt C, Artificial intelligence: the challenge of open systems.
BYTE 1985, April: 223-273.

6. Saaty TL, A scaling method for priorities in hierarchical structures. J Math Psychology 1977; 15:234-281.

7. Saaty TL, Vargas LG. Inconsistency and rank preservation. J Math Psychology 1984; 28:205-214.

8. Frenster JH, Analysis of queueing and renewal within human systems. Nature 1965; 207:1139-1140.

9. Miller GA, The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 1956; 63:81-97.

10. McCarthy J, Mathematical logic in artificial intelligence. In Graubard SR,ed. The artificial intelligence debate: false starts, real foundations. MIT Press, Cambridge, MA 1988: 297-311.

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