GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Predicting individual disease risk based on medical history
Proceedings of the 17th ACM conference on Information and knowledge management
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
HealthCareND: leveraging EHR and CARE for prospective healthcare
Proceedings of the 1st ACM International Health Informatics Symposium
A comorbidity network approach to predict disease risk
ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
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
An Ensemble Topic Model for Sharing Healthcare Data and Predicting Disease Risk
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
A Roadmap for Designing a Personalized Search Tool for Individual Healthcare Providers
Journal of Medical Systems
Machine Learning
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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 patient's medical history using ICD-9-CM codes in order to predict future disease risks. CARE uses collaborative filtering methods 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. Also, we apply time-sensitive modifications which make the CARE framework practical for realistic long-term use. 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.