Bruce G. Buchanan

A model of inexact reasoning in medicine

Abstract Medical science often suffers from having so few data and so much imperfect knowledge that a rigorous probabilistic analysis, the ideal standard by which to judge the rationality of a physicians decision, is seldom possible. Physicians nevertheless seem to have developed an ill-defined mechanism for reaching decisions despite a lack of formal knowledge regarding the interrelationships of all the variables that they are considering. This report proposes a quantification scheme which attempts to model the inexact reasoning processes of medical experts. The numerical conventions provide what is essentially an approximation to conditional probability, but offer advantages over Bayesian analysis when they are utilized in a rule-based computer diagnostic system. One such system, a clinical consultation program named mycin , is described in the context of the proposed model of inexact reasoning.

A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries

Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases. We compared NegEx against a baseline algorithm that has a limited set of negation phrases and a simpler notion of scope. In a test of 1235 findings and diseases in 1000 sentences taken from discharge summaries indexed by physicians, NegEx had a specificity of 94.5% (versus 85.3% for the baseline), a positive predictive value of 84.5% (versus 68.4% for the baseline) while maintaining a reasonable sensitivity of 77.8% (versus 88.3% for the baseline). We conclude that with little implementation effort a simple regular expression algorithm for determining whether a finding or disease is absent can identify a large portion of the pertinent negatives from discharge summaries.

Production rules as a representation for a knowledge-based consultation program

The MYCIN system has begun to exhibit a high level of performance as a consultant on the difficult task of selecting antibiotic therapy for bacteremia. This report discusses issues of representation and design for the system. We describe the basic task and document the constraints involved in the use of a program as a consultant. The control structure and knowledge representation of the system are examined in this light, and special attention is given to the impact of production rules as a representation. The extent of the domain independence of the approach is also examined.

Knowledge engineering for medical decision making: A review of computer-based clinical decision aids

Computer-based models of medical decision making account for a large portion of clinical computing efforts. This article reviews representative examples from each of several major medical computing paradigms. These include 1) clinical algorithms, 2) clinical databanks that include analytic functions, 3) mathematical models of physical processes, 4) pattern recognition, 5) Bayesian statistics, 6) decision analysis, and 7) symbolic reasoning or artificial intelligence. Because the techniques used in the various systems cannot be examined exhaustively, the case studies in each category are used as a basis for studying general strengths and limitations. It is noted that no one method is best for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge engineering research particularly attractive. We stress that considerable basic research in medical computing remains to be done and that powerful new approaches may lie in the melding of two or more established techniques.

Dendral and meta-dendral: their applications dimension

The DENDRAL and Meta-DENDRAL programs assist chemists with data interpretation problems. The design of each program is described in the context of the chemical inference problems the program solves. Some chemical results produced by the programs are mentioned.

Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system☆

Abstract This report describes progress in the development of an interactive computer program, termed MYCIN, that uses the clinical decision criteria of experts to advise physicans who request advice regarding selection of appropriate antimicrobial therapy for hospital patients with bacterial infections. Since patients with infectious diseases often require therapy before complete information about the organism becomes available, infectious disease experts have identified clinical and historical criteria that aid in the early selection of antimicrobial therapy. MYCIN gives advice in this area by means of three subprograms: (1) A Consultation System that uses information provided by the physician, together with its own knowledge base, to choose an appropriate drug or combination of drugs; (2) An Explanation System that understands simple English questions and answers them in order to justify its decisions or instruct the user; and (3) A Rule Acquisition System that acquires decision criteria during interactions with an expert and codes them for use during future consultation sessions. A variety of human engineering capabilities have been included to heighten the programs acceptability to the physicians who will use it. Early experience indicates that a sample knowledge base of 200 decision criteria can be used by MYCIN to give appropriate advice for many patients with bacteremia. The system will be made available for evaluation in the clinical setting after its reliability has been shown to approach that of infectious disease experts.

An Artificial Intelligence program to advise physicians regarding antimicrobial therapy

Abstract An antimicrobial therapy consultation system has been developed which utilizes a flexible representation of knowledge. The novel design facilitates interactive advice-giving sessions with physicians. An ability to display reasons for making decisions at the request of the user permits the program to serve a tutorial as well as consultative role. The feasibility of the judgmental rule approach which the program uses has been demonstrated with a limited knowledge base of approximately 100 rules. Its ultimate success as a clinically useful tool depends upon acquisition of additional rules and thus upon co-operation of infectious disease experts willing to improve the programs knowledge base. The techniques for acquisition, representation, and utilization of knowledge, plus considerations of natural language processing, draw upon and contribute to current Artificial Intelligence research.

Principles of rule-based expert systems

Rule-based expert systems are surveyed. The most important considerations are representation and inference. Rule-based systems make strong assumptions about the representation of knowledge as conditional sentences and about the control of inference in one of three ways. The problem of reasoning with incomplete or inexact information is also discussed, as are several other issues regarding the design of expert systems.

An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a models potential to assist a clinician in deciding whether to treat a given patient in the hospital or at home. We examined the error rates of the models when predicting that a given fraction of patients will survive. We examined survival fractions between 0.1 and 0.6. Over this range, each models predictive error rate was within 1% of the error rate of every other model. When predicting that approximately 30% of the patients will survive, all the models have an error rate of less than 1.5%. The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines.

Inductive Policy: The Pragmatics of Bias Selection

This paper extends the currently accepted model of inductive bias by identifying six categories of bias and separates inductive bias from the policy for its selection (theinductive policy). We analyze existing “bias selection” systems, examining the similarities and differences in their inductive policies, and identify three techniques useful for building inductive policies. We then present a framework for representing and automatically selecting a wide variety of biases and describe experiments with an instantiation of the framework addressing various pragmatic tradeoffs of time, space, accuracy, and the cost of errors. The experiments show that a common framework can be used to implement policies for a variety of different types of bias selection, such as parameter selection, term selection, and example selection, using similar techniques. The experiments also show that different tradeoffs can be made by the implementation of different policies; for example, from the same data different rule sets can be learned based on different tradeoffs of accuracy versus the cost of erroneous predictions.