Uncertainty and Decisions in Medical Informatics

Abstract:

This paper presents a tutorial introduction to the handling of uncertainty and decision-making in medical reasoning systems. It focuses on the central role of uncertainty in all of medicine and identifies the major themes that arise in research papers. It then reviews simple Bayesian formulations of the problem and pursues the generalization to the Bayesian network methods that are popular today. Decision making is presented from the decision analysis viewpoint, with brief mention of recently-developed methods. The paper concludes with review of more abstract characterization of uncertainty, and anticipates the growing importance of analytic and “data mining” techniques as growing amounts of clinical data become widely available.

Keywords:

Decision Support - Uncertainty - Bayes - Graph Models - Decision Trees - Influence Diagrams

• References

• 1 Beck JR, Pauker SG. The Markov process in medical prognosis. Medical Decision Making 1983; 3: 419-58.
• 2 Berzuini C, Bellazzi R, Spiegelhalter D. Bayesian networks applied to therapy monitoring. In: Uncertainty in Artificial Intelligence ’91. Menlo Park, CA: Morgan-Kaufmann; 1991: 35-43.
• 3 Cooper GF. NESTOR: A Computer-based Medical Diagnostic Aid that Integrates Causal and Probabilistic Knowledge. Doctoral Thesis Stanford University; 1984
• 4 Cooper GF. The computational complexity of probabilistic inference using Bayesian Belief Networks. Artif Intell 1990; 42: 393-405.
• 5 Cooper GF, Herskovits E. A Bayesian method for constructing Bayesian Belief Networks from databases. In: Uncertainty in Artificial Intelligence ’91. Menlo Park, CA: Morgan Kaufmann; 1991: 86-94.
• 6 Duda RO, Hart PE, Nilsson NJ. Subjective Bayesian methods for rule-based inference systems. In: AFIPS Conference Proceedings. National Computer Conference; 1976: 1075-82.
• 7 Elston R, Steward A. A general model for the genetic analysis of pedigree data. Hum Hered 1971; 21: 523-42.
• 8 Genesereth MR, Nilsson NJ. Logical Foundations of Artificial Intelligence. Menlo Park, CA: Morgan Kaufmann; 1987
• 9 Gorry GA, Barnett GO. Sequential diagnosis by computer. J Amer Med Ass 1968; 205: 849-54.
• 10 Gorry G, Kassirer J, Essig A, Schwartz W. Decision analysis as the basis for computeraided management of acute renal failure. Amer J Med 1973; 55: 473-84.
• 11 Haimowitz I. Modeling all dialogue system participants to generate empathetic responses. In: Proceedings of the Fourteenth Annual Symposium on Computer Applications in Medical Care. New York: IEEE Comp Soc Press; 1990: 51-7.
• 12 Halpern JY. An analysis of first-order logics of probability. Artif Intell 1990; 46: 311-50.
• 13 Heckerman D. Probabilistic interpretations for MYCIN’s certainty factors. In: Kanal LN, Lemmer JF. eds. Uncertainty in Artificial Intelligence. Amsterdam: North-Holland; 1986: 167-96.
• 14 Heckerman DE. Probabilistic Similarity Networks. Cambridge, MA: MIT Press; 1991
• 15 Holtzman S. Intelligent Decision Systems. Addison-Wesley; 1989
• 16 Howard R, Matheson J. Influence diagrams. In: Howard R, Matheson J. eds. The Principles and Applications of Decision Analysis. Menlo Park, CA: Strategic Decisions Group; 1984: 719-62.
• 17 Jacquez JA. ed. Computer Diagnosis and Diagnostic Methods. Springfield: C. C. Thomas; 1972
• 18 Jeffrey RC. The Logic of Decision. Chicago: University of Chicago Press; 1983
• 19 Kjærulff U. Triangulations of Graphs-Algorithms Giving Small Total State Space. Institute for Electronic Systems. University of Aalborg; Denmark: 1990
• 20 Lange A, Elston R. Extensions to pedigree analysis. Hum Hered 1975; 25: 95-105.
• 21 Leong T-Y. Dynamic decision modeling in medicine: A critique of existing formalisms. In: Symposium on Computer Applications in Medical Care. New York: IEEE Comp Soc Press; 1993: 478-84.
• 22 Long W. The probability of disease. In: Symposium on Computer Applications in Medical Care. New York: IEEE Comp Soc Press; 1991: 619-23.
• 23 Miller R, Pople HE, Myers J. INTERNIST-I, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 1982; 307: 468-76.
• 24 Pauker S, Gorry GA, Kassirer J, Schwartz W. Towards the simulation of clinical cognition: taking a present illness by computer. Amer J Med 1976; 60: 981-96.
• 25 Pauker SG, Kassirer JP. Medical progress: decision analysis. N Engl J Med 1987; 316: 250-8.
• 26 Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann; 1988
• 27 Pearl J, Verma TS. A theory of inferred causation. In: Principles of Knowledge Representation and Reasoning, KR ’91. Menlo Park, CA: Morgan Kaufmann; 1991: 441-52.
• 28 Raiffa H. Decision Analysis: Introductory Lectures on Choices Under Uncertainty. Reading, MA: Addison-Wesley; 1968
• 29 Reggia JA, Nau DS, Wang PY. Diagnostic expert systems based on a set covering model. Intern J Man-Mach Stud 1983; 19: 437-60.
• 30 Rieger C, Grinberg M. The declarative representation and procedural simulation of causality in physical mechanisms. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence 1977; 250-6.
• 31 Savage L. The Foundations of Statistics. New York: Dover Publications; 1972
• 32 Shachter RD. Evaluating influence diagrams. Operations Res 1986; 34: 871-82.
• 33 Shachter RD, Kenley CR. Gaussian influence diagrams. Management Science 1989; 35: 527-50.
• 34 Shachter RD, Peot MA. Simulation approaches to general probabilistic inference on belief networks. In: Henrion M, Shachter RD, Kanal LN, Lemmer JF. eds. Uncertainty in Artificial Intelligence 5. Amsterdam: North-Holland; 1990: 221-31.
• 35 Shafer G. A Mathematical Theory of Evidence. Princeton University Press; 1976
• 36 Shortliffe E. MYCIN: Computer-based Medical Consultations. New York: American Elsevier; 1976
• 37 Spiegelhalter DJ. Bayesian Analysis in Expert Systems. MRC Biostatistics Unit, Institute of Public Health; Cambridge: 1992
• 38 Szolovits P, Pauker S. Categorical and probabilistic reasoning in medicine revisited. Artif Intell 1993; 59: 167-80.
• 39 Wellman MP. Fundamental concepts of qualitative probabilistic networks. Artific Intell 1990; 44: 257-304.
• 40 Wellman MP, Eckman MH, Fleming C. et al. Automated critiquing of medical decision rees. Med Decis Making 1989; 9: 272-84.
• 41 Wu T. A problem decomposition method for efficient diagnosis and interpretation of multiple disorders. Comput Meth Progr Biomed 1991; 35: 239-50.
• 42 Wu T. Domain structure and the complexity of diagnostic problem solving. In: Proceedings of AAAI-91 1991; 855-61.
• 43 Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1978; 1: 3-28.