Publications
Testing Pattern Recognition as a Method for Measuring Severity of Illness
11/05/1989
Testing Pattern Recognition as a Method for Measuring Severity of Illness
D. Trace, M.D.1, F. Naeymi-Rad, M.S.1, L. Carmony, Ph.D.2, S. Chen, M.S.3,
K. Kerns, B.S.1, P. Yarnold, Ph.D.4, M. Tan, M.D.1, M. Astiz, M.D.1, C. Mecher,M.D.1,
M.H. Weil, M.D.1, and M. Evens, Ph.D.3
1University of Health Sciences/The Chicago Medical School, North Chicago, Illinois 60064
2Lake Forest College, Department of Mathematics and Computer Science,
Lake Forest, Illinois 60045
3Illinois Institute of Technology, Department of Computer Science, Chicago, Illinois 60616
4Northwestern University Medical School, Section of General Internal Medicine,
Chicago, Illinois 60611
Abstract
This paper describes a multimembership Bayesian index of severity calculated by MEDAS (the Medical Emergency Decision Assistance System). This severity index measures the likelihood that the patient will die without immediate intervention. The MEDAS inference engine operates on binary features representing signs, symptoms, and laboratory results. As a basis for calculation of the severity index, severity weights, ranging from 0 to 9 were assigned to each feature by an expert physician in order to form a severity pattern. To evaluate the MEDAS severity index, two physician experts in critical care medicine independently provided severity assessments for a series of patients hospitalized for congestive heart failure (N=19) or diabetes mellitus (N=22). These disorders were selected because they are common in the patient load at our VA hospital and lead to a wide range of outcomes. Agreement between the MEDAS severity index and the expert assessments was at or was close to the theoretical maximum.
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