Use abstracted patient-specific features to assist an information-theoretic measurement to assess similarity between medical cases

Authors:
Hui Cao;Genevieve B. Melton;Marianthi Markatou;George Hripcsak
Affiliations:
Department of Biomedical Informatics, Columbia University, 622 W168th, VC5, New York, NY 10032, USA and Health Science & Government, Deloitte Consulting LLP, 200 Clarendron St. Boston, MA 02116, U ...;Department of Surgery, Johns Hopkins Medical Institutions, USA;Department of Biostatistics, Columbia University, 622 W168th, New York, NY 10032, USA;Department of Biomedical Informatics, Columbia University, 622 W168th, VC5, New York, NY 10032, USA
Venue:
Journal of Biomedical Informatics
Year:
2008

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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.