Abstract
Evoking strength is one of the important contributions of the field of Biomedical
Informatics to the discipline of Artificial Intelligence. The University at Buffalo's
Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis
of knee problems and to thereby facilitate referral to the right orthopedic subspecialist.
They had two independent sports medicine physicians review 469 cases. A board-certified
orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a
gold standard diagnosis was reached. For each case, the patients entered 126 potential
answers to 26 questions into a Web interface. These were modeled by an expert sports
medicine physician and the answers were reviewed by L.B. For each finding, the clinician
specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic
evoking strength (ES). Heuristics are methods of reasoning with only partial evidence.
An expert system was constructed that reflected the posttest odds of disease-ranked
list for each case. We compare the accuracy of using Sp to that of using ES (original
model, p < 0.0008; term importance * disease importance [DItimesTI] model, p < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI
model was superior to the use of ES. By the fifth diagnosis, the advantage was lost
and so there is no difference between the techniques when serving as a reminder system.
Keywords
clinical decision support - process management tools - specific types - clinical information
systems - computer-assisted diagnosis - new diagnosis - disease management - clinical
informatics - professional training - education - decision support algorithm - computer-assisted
decision making - knowledge modeling and representation - knowledge management - clinical
information
