title: Use abstracted patient-specific features to assist an information-theoretic measurement to assess similarity between medical cases.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, Columbia University, 622 W168th, VC5, New York, NY 10032, USA. hui.cao@dbmi.columbia.edu
authors.forename: Hui
authors.initials: H
authors.lastname: Cao
authors.forename: Genevieve B
authors.initials: GB
authors.lastname: Melton
authors.forename: Marianthi
authors.initials: M
authors.lastname: Markatou
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
mesh_terms: D000043:Abstracting and Indexing; D007257:Information Theory; D011336:Probability