Abstract
There are a number of obstacles to successful operationalization of clinical practice guidelines, including the difficulty in accurately representing a statement's decidability or an action's executability. Both require reasoning with incomplete and imprecise information, and we present one means of processing such information. We begin with a brief overview of fuzzy set theory, in which elements can have partial memberships in multiple sets. With fuzzy inferencing, these sets can be combined to create multiple conclusions, each with varying degrees of truth. We demonstrate a fuzzy model developed from a published clinical practice guideline on the management of first simple febrile seizures. Although the creation of fuzzy sets can be an arbitrary process, we believe that fuzzy inferencing is an effective tool for the expression of guideline recommendations, and that it can be useful for the management of imprecision and uncertainty.
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Selected References
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- Adlassing K. P., Kolarz G., Scheithauer W., Effenberger H., Grabner G. CADIAG: approaches to computer-assisted medical diagnosis. Comput Biol Med. 1985;15(5):315–335. doi: 10.1016/0010-4825(85)90014-9. [DOI] [PubMed] [Google Scholar]
- Binaghi E., De Giorgi O., Maggi G., Motta T., Rampini A. Computer-assisted diagnosis of postmenopausal osteoporosis using a fuzzy expert system shell. Comput Biomed Res. 1993 Dec;26(6):498–516. doi: 10.1006/cbmr.1993.1036. [DOI] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention (CDC) Asthma mortality and hospitalization among children and young adults--United States, 1980-1993. MMWR Morb Mortal Wkly Rep. 1996 May 3;45(17):350–353. [PubMed] [Google Scholar]
- Fathi-Torbaghan M., Meyer D. MEDUSA: a fuzzy expert system for medical diagnosis of acute abdominal pain. Methods Inf Med. 1994 Dec;33(5):522–529. [PubMed] [Google Scholar]
- Peters R. M., Shanies S. A., Peters J. C. Fuzzy cluster analysis of positive stress tests, a new method of combining exercise test variables to predict extent of coronary artery disease. Am J Cardiol. 1995 Oct 1;76(10):648–651. doi: 10.1016/s0002-9149(99)80190-8. [DOI] [PubMed] [Google Scholar]
- Schäublin J., Derighetti M., Feigenwinter P., Petersen-Felix S., Zbinden A. M. Fuzzy logic control of mechanical ventilation during anaesthesia. Br J Anaesth. 1996 Nov;77(5):636–641. doi: 10.1093/bja/77.5.636. [DOI] [PubMed] [Google Scholar]
- Shiffman R. N., Greenes R. A. Improving clinical guidelines with logic and decision-table techniques: application to hepatitis immunization recommendations. Med Decis Making. 1994 Jul-Sep;14(3):245–254. doi: 10.1177/0272989X9401400306. [DOI] [PubMed] [Google Scholar]
- Shiomi S., Kuroki T., Jomura H., Ueda T., Ikeoka N., Kobayashi K., Ikeda H., Ochi H. Diagnosis of chronic liver disease from liver scintiscans by fuzzy reasoning. J Nucl Med. 1995 Apr;36(4):593–598. [PubMed] [Google Scholar]
- Tierney W. M., Overhage J. M., Takesue B. Y., Harris L. E., Murray M. D., Vargo D. L., McDonald C. J. Computerizing guidelines to improve care and patient outcomes: the example of heart failure. J Am Med Inform Assoc. 1995 Sep-Oct;2(5):316–322. doi: 10.1136/jamia.1995.96073834. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ying H., McEachern M., Eddleman D. W., Sheppard L. C. Fuzzy control of mean arterial pressure in postsurgical patients with sodium nitroprusside infusion. IEEE Trans Biomed Eng. 1992 Oct;39(10):1060–1070. doi: 10.1109/10.161338. [DOI] [PubMed] [Google Scholar]