Combining evidence in hybrid medical decision support models.
Hybrid methods are particularly useful for building diagnostic models based on biomedical data due to the wide variety of data types that are routinely encountered. Evaluation of the effectiveness of hybrid systems is complicated when multiple methods are combined to reach a conclusion. In the work described here, methods for combining results based on the general reliability of each model as well as its applicability to the case under evaluation are presented. Reliability measures differ depending on whether symbolic or numeric information is analyzed and depend on the strength of the decision algorithm as well as the soundness of the domain knowledge upon which the decision is based. In addition to reliability, combination of results is complicated by the need to weight each method to form the final conclusion. Weighting factors depend on the degree of certainty that the decision is correct for each of the methods. The process is illustrated in an application to cardiac diagnosis.
Cohen
Maurice E
ME
University of California, San Francisco. mcohen@fresno.ucsf.edu
Hudson
Donna L
DL
D000465:Algorithms; D002140:California; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D019317:Evidence-Based Medicine / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D016347:Medical Records Systems, Computerized; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface