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Appl Clin Inform 2018; 09(02): 432-439
DOI: 10.1055/s-0038-1656547
Research Article
Schattauer GmbH Stuttgart

Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support

Peter L. Elkin
1   Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
,
Daniel R. Schlegel
1   Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
,
Michael Anderson
2   Department of Orthopedics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
,
Jordan Komm
1   Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
2   Department of Orthopedics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
,
Gregoire Ficheur
1   Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
,
Leslie Bisson
2   Department of Orthopedics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States
› Author Affiliations Funding Funding for this project was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under award number UL1TR001412. The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH. This work has been supported in part by a training grant from the National Library of Medicine LM1012495.
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Publication History

06 February 2018

17 April 2018

Publication Date:
13 June 2018 (online)

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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

Protection of Human and Animal Subjects

The study obtained IRB approval # 1690612E.


 

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