GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The nature of statistical learning theory
The nature of statistical learning theory
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Applying collaborative filtering techniques to movie search for better ranking and browsing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Recommender systems are widely used to provide users with personalized suggestions for products or services. These systems typically rely on collaborative filtering (CF) to make automated predictions about the interests of a user, by collecting preference information from many users. CF techniques require no domain knowledge and can be used on very sparse datasets. Moreover, they rely directly on user behavior and are able to potentially discover complex and unexpected patterns that are difficult or impossible to profile using known data attributes. In this paper, we explore the use of a CF framework for clinical risk stratification. Our work assesses patient risk both by matching new cases to historical records, and by matching patient demographics to adverse outcomes. When evaluated on data from over 4,500 patients admitted with acute coronary syndrome, our CF-based approach achieved a higher predictive accuracy for both sudden cardiac death and recurrent myocardial infraction than popular classification approaches such as logistic regression and support vector machines.