Fuzzy logic in clinical practice decision support systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a derision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners.

[1]  G O Barnett,et al.  “Just‐in‐time” clinical information , 1997, Academic medicine : journal of the Association of American Medical Colleges.

[2]  Parvati Dev,et al.  A new instrument for medical decision support and education: the Stanford Health Information Network for Education , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[3]  G O Barnett,et al.  An architecture for a distributed guideline server. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[4]  George J. Klir,et al.  Uncertainty-Based Information , 1999 .

[5]  Peter Johnson,et al.  Algorithm and Care Pathway: Clinical Guidelines and Healthcare Processes , 1997, AIME.

[6]  Robert A. Greenes,et al.  Research Paper: The GuideLine Interchange Format: A Model for Representing Guidelines , 1998, J. Am. Medical Informatics Assoc..

[7]  Richard N. Shiffman,et al.  Model Formulation: Representation of Clinical Practice Guidelines in Conventional and Augmented Decision Tables , 1997, J. Am. Medical Informatics Assoc..

[8]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[9]  Gleb Beliakov,et al.  Chronic disease coordinated care planning: flexible, task-centered decision support , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[10]  Gleb Beliakov,et al.  APPROXIMATE REASONING AND INTERPRETATION OF LABORATORY TESTS IN MEDICAL DIAGNOSTICS , 1995 .

[11]  Ramon López de Mántaras,et al.  Approximate Reasoning Models , 1990 .

[12]  Stephen B. Johnson,et al.  Medical decision support: experience with implementing the Arden Syntax at the Columbia-Presbyterian Medical Center. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[13]  A. Lansing,et al.  Canadian Medical Association , 1889, British medical journal.

[14]  Iain E. Buchan,et al.  WAX ActiveLibrary; a tool to manage information overload , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[15]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[16]  Derek A. Linkens,et al.  Self-Learning Fuzzy Logic Control in Medicine , 1997, AIME.

[17]  J M Grimshaw,et al.  Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. , 1994, Lancet.

[18]  T. Clemmer,et al.  A computer-assisted management program for antibiotics and other antiinfective agents. , 1998, The New England journal of medicine.

[19]  Matthias Scherf,et al.  A Neuro-Fuzzy-Classifier for a Knowledge-Based Glaucoma Monitor , 1997, AIME.

[20]  Marie-Christine Jaulent,et al.  Automatic detection of cardiac contours on MR images using fuzzy logic and dynamic programming , 1997, AMIA.

[21]  Klaus-Peter Adlassnig,et al.  Fuzzy Set Theory in Medical Diagnosis , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  Parvati Dev,et al.  The Stanford Health Information Network for Education: integrated information for decision making and learning , 1997, AMIA.

[23]  Richard N. Shiffman,et al.  Operationalization of clinical practice guidelines using fuzzy logic , 1997, AMIA.

[24]  R A Greenes,et al.  Representation of clinical practice guidelines through an interactive World-Wide-Web interface. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[25]  J. Eisenberg Agency for Health Care Policy and Research. , 1999, Medical care.

[26]  L L Leape,et al.  Practice guidelines and standards: an overview. , 1990, QRB. Quality review bulletin.

[27]  D. Slawson,et al.  What clinical information do doctors need? , 1997 .

[28]  J. Kacprzyk,et al.  Logical Structures for Representation of Knowledge and Uncertainty , 1998 .

[29]  R. Zielstorff Online practice guidelines: issues, obstacles, and future prospects. , 1998, Journal of the American Medical Informatics Association : JAMIA.

[30]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[31]  Lam Sh Implementation and evaluation of practice guidelines. , 1993 .

[32]  D F Lobach,et al.  A model for adapting clinical guidelines for electronic implementation in primary care. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[33]  J. Grimshaw,et al.  Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations , 1993, The Lancet.

[34]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[35]  Gleb Beliakov,et al.  Aggregation operators as similarity relations , 2000 .