From netflix to heart attacks

Abstract

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.