A Prediction System for Cardiovascularity Diseases Using Genetic Fuzzy Rule-Based Systems
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Affinity analysis of coded data sets
Proceedings of the 2009 EDBT/ICDT Workshops
Time to CARE: a collaborative engine for practical disease prediction
Data Mining and Knowledge Discovery
Combining markov models and association analysis for disease prediction
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
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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 uses collaborative filtering 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 Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.