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Text Mining and Patient Severity Clusters

Text Mining and Patient Severity Clusters

Patricia Cerrito
Copyright: © 2010 |Pages: 54
ISBN13: 9781605667522|ISBN10: 1605667528|ISBN13 Softcover: 9781616924515|EISBN13: 9781605667539
DOI: 10.4018/978-1-60566-752-2.ch008
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MLA

Patricia Cerrito. "Text Mining and Patient Severity Clusters." Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons, IGI Global, 2010, pp.287-340. https://doi.org/10.4018/978-1-60566-752-2.ch008

APA

P. Cerrito (2010). Text Mining and Patient Severity Clusters. IGI Global. https://doi.org/10.4018/978-1-60566-752-2.ch008

Chicago

Patricia Cerrito. "Text Mining and Patient Severity Clusters." In Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-752-2.ch008

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Abstract

Text mining diagnosis codes takes advantage of the linkage across patient conditions instead of trying to force the assumption of independence. Combinations of diagnoses are used to define groups of patients. For example, patients with diabetes have a high probability of heart disease and kidney failure compared to the general population. Instead of relying on these three conditions and assuming that the general population is just as likely to acquire them in combination, text mining examines the combinations of diabetes, diabetes with kidney failure, diabetes with heart failure, and diabetes with both conditions.

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