An epistemological framework for medical knowledge-based systems

An abstraction paradigm for unifying different perspectives concerning the analysis and design of knowledge-based systems (KBSs) is presented. The model accounts for all of the conceptual features of knowledge-based systems, thus making clear which features are intrinsic to the problem and which are artifacts of the implementation. The proposal is based on a two-level analysis of knowledge-based systems: an epistemological and a computational level. At the first level, ontology and inference models of a knowledge-based system are defined. At the computational level, methods and formalisms are adopted after the epistemological analysis has been carried out. The study is confined to medicine with three generic tasks identified: diagnosis, therapy planning, and monitoring. The results of this analysis indicate that the generic tasks manage different ontologies, but can be executed exploiting a unique inference model. Computational issues are discussed to argue that the model provides a conceptual view on existing systems and some design insights for future ones. >

[1]  Luc Steels,et al.  Components of Expertise , 1990, AI Mag..

[2]  K. VanLehn.  Problem solving and cognitive skill acquisition , 1989 .

[3]  Elpida T. Keravnou,et al.  Expert systems architectures , 1988 .

[4]  Hector J. Levesque,et al.  Expressiveness and tractability in knowledge representation and reasoning 1 , 1987, Comput. Intell..

[5]  James F. Allen.  Towards a General Theory of Action and Time , 1984, Artif. Intell..

[6]  Steen Andreassen,et al.  A munin network for the median nerve - a case study on loops , 1989, Appl. Artif. Intell..

[7]  Peter Szolovits,et al.  Representing Medical Knowledge in a Terminological Language is Difficult , 1988 .

[8]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[9]  E H Shorthffe,et al.  Computer-based medical consultations mycin , 1976 .

[10]  Janice S. Aikins.  Prototypical Knowledge for Expert Systems: A Retrospective Analysis , 1993, Artif. Intell..

[11]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[12]  Robert Milne,et al.  Strategies for Diagnosis , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  J. McCarthy.  Circumscription|a Form of Nonmonotonic Reasoning , 1979 .

[14]  Drew McDermott,et al.  Temporal Data Base Management , 1987, Artif. Intell..

[15]  Michael J. Freiling,et al.  Knowledge Level Engineering Ontological Analysis , 1986, AAAI.

[16]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[17]  D. McDermott.  A Temporal Logic for Reasoning About Processes and Plans , 1982, Cogn. Sci..

[18]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[19]  C. P. Langlotz.  A Decision-theoretic approach to heuristic planning , 1989 .

[20]  Michael R. Genesereth,et al.  The Use of Design Descriptions in Automated Diagnosis , 1984, Artif. Intell..

[21]  Jon Doyle,et al.  Constructive belief and rational representation , 1989, Comput. Intell..

[22]  Paul R. Cohen,et al.  Design for Acquisition: Principles of Knowledge-System Design to Facilitate Knowledge Acquisition , 1987, Int. J. Man Mach. Stud..

[23]  M Stefanelli,et al.  A performance evaluation of the expert system ANEMIA. , 1988, Computers and biomedical research, an international journal.

[24]  B. Chandrasekaran,et al.  Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design , 1986, IEEE Expert.

[25]  Eric Horvitz,et al.  Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base Part II , 2016 .

[26]  E. Rosch,et al.  Family resemblances: Studies in the internal structure of categories , 1975, Cognitive Psychology.

[27]  Benjamin Kuipers,et al.  Qualitative Simulation as Causal Explanation , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[28]  Diana E. Forsythe,et al.  Knowledge acquisition for expert systems: some pitfalls and suggestions , 1989, IEEE Trans. Syst. Man Cybern..

[29]  John McCarthy,et al.  Circumscription - A Form of Non-Monotonic Reasoning , 1980, Artif. Intell..

[30]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[31]  E. Shortliffe.  Computer-based medical consultations: mycin (elsevier north holland , 1976 .

[32]  B. Chandrasekaran,et al.  Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples , 1988, The Knowledge Engineering Review.

[33]  S. Norell,et al.  Models of Causation in Epidemiology , 1984 .

[34]  Patrick J. Hayes,et al.  Naive physics I: ontology for liquids , 1989 .

[35]  B. Chandrasekaran,et al.  Conceptual Representation of Medical Knowledge for Diagnosis by Computer: MDX and Related Systems , 1983, Adv. Comput..

[36]  Larry Eshelman,et al.  MOLE: A Knowledge-Acquisition Tool for Cover-and-Differentiate Systems , 1988 .

[37]  David C. Wilkins,et al.  An analysis of the distinction between deep and shallow expert systems , 1989 .

[38]  F. Kohler,et al.  C. Truesdell: An Idiot's Fugitive Essays on Science - Methods, Criticism, Training, Circumstances, Springer-Verlag: Berlin, Heidelberg, New York, Tokyo 1984. 654 Seiten, Preis: DM 158,—. , 1986 .

[39]  Steen Andreassen,et al.  MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings , 1987, IJCAI.

[40]  M Stefanelli,et al.  NEOANEMIA: a knowledge-based system emulating diagnostic reasoning. , 1990, Computers and biomedical research, an international journal.

[41]  中園 薫.  A Qualitative Physics Based on Confluences , 1986 .

[42]  Douglas B. Lenat,et al.  Ontological Versus Knowledge Engineering , 1989, IEEE Trans. Knowl. Data Eng..

[43]  H. P Nii,et al.  Blackboard Systems , 1986 .

[44]  Giordano Lanzola,et al.  Qualitative models in medical diagnosis , 1990, Artif. Intell. Medicine.

[45]  J. Doyle,et al.  Artificial Intelligence and Rational Self-Government , 1988 .

[46]  M. Chi,et al.  The Nature of Expertise , 1988 .

[47]  Judea Pearl,et al.  A Computational Model for Causal and Diagnostic Reasoning in Inference Systems , 1983, IJCAI.

[48]  John McDermott,et al.  Preliminary steps toward a taxonomy of problem-solving methods , 1993 .

[49]  T. Russ,et al.  Using hindsight in medical decision making. , 1989, Computer methods and programs in biomedicine.

[50]  Barbara Hayes-Roth,et al.  A Blackboard Architecture for Control , 1985, Artif. Intell..

[51]  Peter Szolovits,et al.  Causal Understanding of Patient Illness in Medical Diagnosis , 1981, IJCAI.

[52]  Janet L. Kolodner,et al.  Using Experience in Clinical Problem Solving: Introduction and Framework , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[53]  Douglas B. Lenat,et al.  CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowledge Acquisition Bottlenecks , 1986, AI Mag..

[54]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[55]  Benjamin J. Kaipers,et al.  Qualitative Simulation , 1989, Artif. Intell..

[56]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artificial Intelligence.

[57]  Sandra Marcus,et al.  SALT: A Knowledge-Acquisition Tool for Propose-and-Revise Systems , 1988 .

[58]  Ren‐Zong Qiu,et al.  Models of explanation and explanation in medicine , 1989 .

[59]  Johan de Kleer,et al.  An Assumption-Based TMS , 1987, Artif. Intell..

[60]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[61]  James A. Reggia,et al.  Diagnostic Expert Systems Based on a Set Covering Model , 1983, Int. J. Man Mach. Stud..

[62]  David Poole,et al.  Explanation and prediction: an architecture for default and abductive reasoning , 1989, Comput. Intell..

[63]  Barbara Hayes-Roth,et al.  Intelligent Monitoring and Control , 1989, IJCAI.

[64]  J. Kassirer,et al.  Taking a Present Illness by Computer , 1976 .

[65]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[66]  Yun Peng,et al.  A Probabilistic Causal Model for Diagnostic Problem Solving Part I: Integrating Symbolic Causal Inference with Numeric Probabilistic Inference , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[67]  Dean Allemang,et al.  The Computational Complexity of Abduction , 1991, Artif. Intell..

[68]  William J. Clancey,et al.  Heuristic Classification , 1986, Artif. Intell..

[69]  L. Shulman,et al.  Medical Problem Solving: An Analysis of Clinical Reasoning , 1978 .

[70]  B. Chandrasekaran,et al.  Deep versus Compiled Knowledge Approaches to Diagnostic Problem-Solving , 1982, Int. J. Man Mach. Stud..

[71]  R A Miller,et al.  QUICK (QUick Index to Caduceus Knowledge): using the INTERNIST-1/CADUCEUS knowledge base as an electronic textbook of medicine. , 1985, Computers and biomedical research, an international journal.

[72]  Elpida T. Keravnou,et al.  Deep and shallow models in medical expert systems , 1989, Artif. Intell. Medicine.

[73]  M Stefanelli,et al.  ANEMIA: an expert consultation system. , 1986, Computers and biomedical research, an international journal.

[74]  John F. Sowa,et al.  Conceptual Structures: Information Processing in Mind and Machine , 1983 .

[75]  Jon Sticklen,et al.  'Deep' models and their relation to diagnosis , 1989, Artif. Intell. Medicine.

[76]  Barbara Hayes Roth.  Architectural foundations for real-time performance in intelligent agents , 1990 .

[77]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[78]  S. Weiss,et al.  Glaucoma consultation by computer. , 1978, Computers in biology and medicine.

[79]  H. E. Stephanou,et al.  Perspectives on imperfect information processing , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[80]  Riccardo Bellazzi,et al.  Therapy Planning by Combining Ai and Decision Theoretic Techniques , 1989, AIME.