Cognition and decision in biomedical artificial intelligence: From symbolic representation to emergence

Abstract

This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies of induction and deduction, and by software abilities to dialogue, co-operate and function as a cognitive extension of the physician's intellectual capabilities. The proposed methodology gives the expert a prominent role which consists, first, of faithfully enunciating the descriptive features of his medical knowledge, thus giving the computer a precise description of his own perception of basic medicine, and secondly, of painstakingly gathering patients into computerised case bases which simulate exhaustive long-term memory. The AI capacities for knowledge elaboration are then triggered, giving rise to mathematically optimal diagnoses, prognoses, or treatment protocols which the physician may then evaluate, accept, reject, or adapt at his convenience, and finally append to a knowledge base. The idea of emergence embraces many issues concerning the purpose and intent of artificial intelligence in medical practice. Particularly, we address the representation problem as it is raised by classical decisional knowledge-based systems, and develop the notions of classifiers and hybrid systems as possible answers to this problem. Finally, since the whole methodology touches the problem of technological investment in health care, now inherent in modern medical practice, some ethical considerations accompany the exposé.

This is a preview of subscription content, log in to check access.

References

  1. Amy, B. (1989). Contextual cognitive machines. In Tiberghien, G (ed.)Advancees in cognitive sciences. Vol 2: Theory and applications. Ellis Horwood. New York.

    Google Scholar 

  2. Amy, B. Giacometti, A., and Gut, A. (1990). Modèles connexionnistes de l'expertise. InProceed. NEURO NIMES '90. EC2. Nanterre, France. 99–119.

  3. Andler, D. (ed.), (1992)Introduction aux sciences cognitives. Gallimard. Paris.

    Google Scholar 

  4. Barnett, G.O., Cimino, J.J., Hupp, J.A. and Hoffer, E.P. (1987). Dxplain: An evolving diagnostic decision support system. J Am.Med. Assoc.,3. 258.

    Google Scholar 

  5. Barsalou, L.W. and Hale, C.R., (1993) Component of conceptual representations: from feautres lists to recursive frames. In Van Mechelen, I. et al. (ed.)Categories and Concepts. Academic Press, New York.

    Google Scholar 

  6. Berner, E.S. Webster, G.D. Shugerman, A.A. Jackson, J.R. Algina, J., and Baker, A.L. (1994). Performance of four computer-based diagnostic systems.New England Journal of Medicine,330 (25). 1792–1796.

    Article  Google Scholar 

  7. Beuscart, R., and Fieschi, M. (1992). Cognition et systèms experts medicaux.Le courrier du CNRS,79. 101.

    Google Scholar 

  8. Brooks, R.A. (1991). Intelligence without representation.Artificial intelligences,47. 139–159.

    Article  Google Scholar 

  9. CCE (1992). EPIRIT:Specific Search and technological development in the field of information technology-Results and Progress 1991–1992. Commission of the European Communities. Luxembourg.

    Google Scholar 

  10. Changeux, J.-P., and Dahaene, S. (1989). Neuronal Models of Cognitive Functions.Cognition,33. 63–109.

    Article  Google Scholar 

  11. Clancey, W.J. (1985). Heuristic Classification.Artificial Intelligence,27. 289–350.

    Article  Google Scholar 

  12. Demongeot, J., Goles, E., and Tchuente, M. (ed.), (1985),Dynamical Systems and Cellular Automata. Academic Press, New York.

    Google Scholar 

  13. Demongeot, J., Hérve, T., Rialle, V. and Roche, C. (ed), (1988).Artificial Intelligence and Cognitive Sciences. Manchester University Press. New York.

    Google Scholar 

  14. Demongeot, J., Le Beuax, P., and Weil, G. (ed.), (1994).Informatisation de l'Unité de Soins du Futur. Springer-Verlag, Paris.

    Google Scholar 

  15. Dennett, D.C. (1991).Consciousness explained. Little-Brown-and-Co. Boston.

    Google Scholar 

  16. Feldman, J.A. (1989). Connectionist Representation of Concepts. In Pfeifer, R. et al. (ed.)Connectionism in perspective. North-Holland Publ. Amsterdam, The Netherlands. 24–45.

    Google Scholar 

  17. Forrest, D. (ed.), (1991)Emergent Computation. MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  18. François, P. Robert, C., Cremilleux, B., Bucharles, C., and Demongoat, J. (1990). Variables processing in expert system building: Application to the aetiological diagnosis of infantile meningitis.Medical Informatics,15(2). 115–124.

    Article  Google Scholar 

  19. Gallant, S.I. (1988) Connectionist expert systems.Communications of the ACM,2(31). 152–169.

    Article  Google Scholar 

  20. Giacommeti, A., Iordanova, I., Amy, B., Vila, A., Reymond, F., Abaoub, L., Dahou, M., and Rialle, V. (1992). A Hybrid Approach to Computer Aided Diagnosis in Electromyography.14th Int Conf IEEE Engin Med Biol Soc,14. 1012–1013.

    Google Scholar 

  21. Grémy, F. (1988). Persons and Computers in Medicine and Health.Methods of Information in Medicine,27. 3–9.

    Google Scholar 

  22. Grémy, F. (1989). Human meaning of medical informatics: reflections on its future and trends.Medical Informatics,14(1). 1–11.

    Article  Google Scholar 

  23. Hendler, J.A. (1989). On the need of hybrid systems.Connection Science (special issue on Hybrid Connectionist/Symbolic Systems),1(3).

  24. Henery, R.J. King, R., Sutherland., Mitchell, J.M.O., and Brazdil, P. (1991). Statlog: Comparative Testing of Statistical and machine Learning Algorithms. InESPIRIT-Information Processing and Software: Results and Progress of Selected Projects. DG XIII. C.E.E. Bruxelles. 398–411.

  25. Holland, J.H. (1986). Escaping brittleness: The Possibilities of General Purpose Learning Algorithmes Applied to Parallel Rule-Based Systems. In Michalski, R., Carbonel, J., and Mitchel, T. (ed.)Machine Learning. 2. Morgan-Kaufmann. Sen Mateo, CA. 594–623.

    Google Scholar 

  26. Hopfield, J.J. (1982). Neural Networks and physical Systems with Emergent Collective Computational Abilities.Proceedings of the National Academy of Science,79. 2554–2558.

    MathSciNet  Article  Google Scholar 

  27. Iordanova, I., Rialle, V., and Vila, A. (1992). Use of Unsupervised Neural Networks for Classification Tasks in Electromyographgy. Proc.14th Int Conf IEEE Engin Med Biol Soc,14. 1014–1015.

    Article  Google Scholar 

  28. Kulikovski, C.A. and Weiss, S.M. (1982). Representation of expert knowledge for consultation: The Casnet and Expert projects. In Szolovits, P. (ed.)Artificial Intelligence in Medicine. Westview Press. Colarado.

    Google Scholar 

  29. Memmi, D. (1989). Connectionism and Artificial Intelligence. In Proceed.Neuro-Nimes '89. EC2. Nanterre, France. 17–34.

  30. Meunier, J.G. (1992). Le Probléme de la catégorisation dans la representation de connaissances.Intellectica, (13–14). 353–356.

    Google Scholar 

  31. Meunier, J.G. (1993). Semiotic Primitives and conceptual representation of knowledge. In Jorna, R.J., Van Heusdenm, B., and Posner, R. (ed.)Sign, Search and Communication. Walter de Gruter. Berlin.

    Google Scholar 

  32. Miller, R.A., Masarie, F.E., Myers, J.D. (1986). Quick Medical Reference (QMR for diagnostic assistance.MD Computing,3 35–48.

    Google Scholar 

  33. MLT-Consortium (1993).MLT Project: Final Public Report. Commission of the European Community. Brussels.

    Google Scholar 

  34. Orsier, B., Iordanova, I., Rialle, V., Giacometti, A., and Vila, A. (1994). Hybrid systems for expertise modelling: from concepts to a medical application in electromyography.Computers and Artificial Intelligence,13(5). 423–440.

    Google Scholar 

  35. Patel, V.L. and Evans, D.A. (ed.), (1989).Cognitive Science in Medicine: Biomedical Modelling. MIT Press. Cambridge, Massachusets.

    Google Scholar 

  36. Patel, V.L. and Groen, G.J. (1987). Knowledge based solution strategies in medical reasoning,.Cognitive Science,10(1). 91–126.

    Article  Google Scholar 

  37. Perry, C. (1990). Knowledge bases in Medicine: a review.Bull. Med. Libr. Assoc.,78(3). 271–282.

    Google Scholar 

  38. Pham, K.M., and Degoulet, P. (1991). MOSAIC: Medical Knowledge Processing Based on a Macro-Connectionist Approach to Neural Networks. InMEDINFO: Proc 6th Congress on Medical Informatics. Vol I. 82–86.

  39. Pinkas, G. (1992). Requested Unrestricted First-Order Logic Formulas in Connectionist Networks. In Sun, R., and Bookman, L. A. (ed.) Proc. of the AAAI-92Workshop «Integrating Neural and Symbolic Processes-The Cognitive Dimension”. San Jose, California, 23–30.

  40. Pollack, J.B. (1990) Recursive Distributed Representations.Artificial Intelligence 46. 77–105.

    Article  Google Scholar 

  41. Posner, M. (ed), (1989).Foundation of Cognitive Science. MIT Press, Cambridge, MA.

    Google Scholar 

  42. Rialle, V. (1988). Data Analysis as an aid to Learning and Knowledge Base Making in a medical field. In Deomongoat, J. et al. (ed.)Artificial Intelligence and Cognitive Sciences. Manchester University Press. New York. 375–386.

    Google Scholar 

  43. Rialle, V. (1989). Pour une intégration des systèmes à bases de connaissance en practique clinique courante. InActes de Journées Francophones d'Informatique Médicale de Montpellier. Editions de l'Ecole Nationale de la Santé Publique, Rennes. 279–285.

    Google Scholar 

  44. Rialle, V. (1994).Décision et Cognition en Biomédicine: Modèles et Intégration. Mémoire de Diplôme d'Habilitation à Diriger des Recherces-informatique. Université Joseph Fourier, Grenoble.

    Google Scholar 

  45. Rialle, V. (1995). Vers la maîtrise informatique de la connaissance. In Joly, H. (ed.)Biomédicine 2000. Lavoiser, Paris. 52–75.

    Google Scholar 

  46. Rialle, V., Ohayon, M., Amy, B., Bessière, P. (1991a) Medical Knowledge Modeling in a Symbolic-Connectionist Perspective. In Nagel, J.H., and Smith, W.M. (ed.) 3th An. Int. Conf. IEEE Engin. in Med. and Biol. Society. 13(3) IEEE. Piscataway, NJ. 1109–1110.

    Google Scholar 

  47. Rialle, V., Pagnosis, D., Vermont, J., Augerat, P., and Giarardet, P. (1991b). Are expert systems able to assist intensive care?: Specific issues in the modeling of intensive care practice. In Pavé A., and Vansteenkiste, G.C. (ed.)Artificial intelligence in numerical and symbolic simulation Aléas, Lyon. 101–113.

    Google Scholar 

  48. Rialle, V., and Payette, D. (ed.), (1994).Modèles de la cognition: vers une science de l'espirit LEKTON, 4(2). Départment de Philosophie. Université du Québec à Montréal.

  49. Rialle, V., and Stip, E. (1994). Modelisation cognitive en psychiatrie: des modèles symboliques aux modèles parallèles et distribués.Journal of Psychiatry and Neuroscience,19(3). 178–192.

    Google Scholar 

  50. Rialle, V., Stip, E., and O'Connor, K. (1994). Computer Mediated Psychotherapy: Ethical issues and difficulties in Implementation.Humane Medicine,10(3). 185–192.

    Google Scholar 

  51. Rialle, V., Vila, A., and Besnard, Y. (1991). Heterogeneous knowledge representation using a finite automaton and first order logic: A case study in Electromyography.Artificial Intelligence in Medicine,3(2). 65–74.

    Article  Google Scholar 

  52. Riesbeck, C.K., and Schank, R.C. (1989).Inside Case-Based Reasoning. Lawrence Erlbaum. Hillsdale, New Jersey.

    Google Scholar 

  53. Rosch, E. (1978). Principles of categorization. In Rosch, E., and Lloyd, B.B. (ed.)Cognition and Categorization. Lawrence Erlbaum. Hillsdale, New jersey.

    Google Scholar 

  54. Rumelhart, D.E. McClelland, J.L., and PDP-Research-Group (ed.), (1986).Parallel Distributed Processing: Exploration in the Microstructure of Cognition. Vol 1: Foundations. MIT Press/Bradford Books. Cambridge, Massachusetts.

    Google Scholar 

  55. Shortliffe, E.H. (1976).Computer-Based Medical Consultation: MYCIN. American-Elsevier. New York.

    Google Scholar 

  56. Smolensky, P. (1986). Information Processing in Dynamical Systems: Foundation of Harmony Theory. In Rummelhart, D.E., McClelland, J.L., and PDP Reasearch-Group (ed.)Parallel Distributed Processing: Exploration in the Microstructure of Cognition. MIT Press. Cambridge, Massachusetts.

    Google Scholar 

  57. Summer, W. (1994). A review of Iliad and Quick Medical Reference for primary care providers. Two diagnostic computer programs.Arch. Fam. Med. 2(1). 87–95.

    Article  Google Scholar 

  58. Sun, R., and Bookman, L.A. (ed.), (1992). Proc. of the AAAI-92 Workshop “Integrating Neural and Symbolic Processes-The Cognitive Dimension”. San Jose, California.

  59. Thornton, C.J. (1992).Techniques in Computational Learning: An Introduction. Chapman & Hall. London.

    Google Scholar 

  60. Tiberghien, G. (ed.), (1989).Advances in cognitive science. Vol. 2. Theory and applications. Ellis Horwood. Chichester.

    Google Scholar 

  61. Touretzky, D.S. (1989). BolzCONS:Dynamic Symbol Structures in a Connectionist Network. Tech. Rep. No. CMU-CS-89-172, Carnegie Mellon University.

  62. Varela, F., Thompson, E., and Rosch, E. (1991).The Embodied Mind: Cognitive Science and Human Experience. MIT Press, Cambridge, M.A.

    Google Scholar 

  63. Varela, F.J. and Bourgine, P. (ed.), (1992)Toward a Practice of Autonomous Systems. Proc. 1st Europ Conf Artificial Life. MIT Press. Cambridge, Massachusetts.

    Google Scholar 

  64. Weiss, S.M., and Kulikowski, C.A. (1991).Computer Systems that Learn: Classficiation and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems, Morgan Kaufmann. San Mateo, California.

    Google Scholar 

  65. Weizenbaum, J. (1976).Computer power and human reason-From judgement to calculation. Freeman. San Francisco, CA.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Vincent Rialle.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Rialle, V. Cognition and decision in biomedical artificial intelligence: From symbolic representation to emergence. AI & Soc 9, 138–160 (1995). https://doi.org/10.1007/BF01210601

Download citation

Keywords

  • Artificial intelligence
  • Medical-informatics
  • Medical information systems
  • Medical decision making
  • Machine-learning
  • Cognitive science
  • Expert systems