Abstract:
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A more accurate patient cost prediction model would facilitate the development of a prospective payment system in healthcare that incentivizes long-term efficiency and effectiveness of care. Patient cost prediction models currently used by healthcare payers are based in linear regression on only a small number of patient demographic and diagnosis variables. This work introduces a methodology for building patient cost prediction models that (1) extract predictive information from extensive electronic health records, (2) identify medical variables prognostic of a patient's future costs, and (3) improve upon the predictive accuracy of linear regression models through the use of shrinkage methods and tree-based machine learning techniques. Cost prediction models were trained, tested, and evaluated for accuracy on a set of patient data from over 30,000 University of Chicago Hospital patients.
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