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Type of Document Master's Thesis Author Davis, Darcy A Author's Email Address ddavis4@nd.edu URN etd-08182008-190502 Title Predicting Individual Disease Risk Based on Medical History Degree Master of Science in Computer Science and Engineering Department Computer Science and Engineering Advisory Committee
Advisor Name Title Kevin W. Bowyer Committee Member Nitesh V. Chawla Committee Member Patrick J. Flynn Committee Member Keywords
- disease prediction
- collaborative filtering
Date of Defense 2008-07-18 Availability unrestricted Abstract The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost efficient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on a patient's medical history using ICD-9-CM codes in order to predict future diseases risks. CARE combines collaborative filtering methods with clustering to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a large Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.Files
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