Use abstracted patient-specific features to assist an information-theoretic measurement to assess similarity between medical cases.
Inter-case similarity metrics can potentially help find similar cases from a case base for evidence-based practice. While several methods to measure similarity between cases have been proposed, developing an effective means for measuring patient case similarity remains a challenging problem. We were interested in examining how abstracting could potentially assist computing case similarity. In this study, abstracted patient-specific features from medical records were used to improve an existing information-theoretic measurement. The developed metric, using a combination of abstracted disease, finding, procedure and medication features, achieved a correlation between 0.6012 and 0.6940 to experts.
Cao
Hui
H
Department of Biomedical Informatics, Columbia University, 622 W168th, VC5, New York, NY 10032, USA. hui.cao@dbmi.columbia.edu
Melton
Genevieve B
GB
Markatou
Marianthi
M
Hripcsak
George
G
D000043:Abstracting and Indexing; D007257:Information Theory; D011336:Probability