Predicting rare events using principal component analysis and partial least squares

摘要

Systems and methods are provided for predicting rare events, such as hospitalization events. Data related to health and/or healthcare may be compiled from a number of sources and used to construct a predictive model. The predictive model employ Principal Component Analysis (PCA) and Partial Least Squares (PLS). The data may be arranged in a timeline, and formatted in such a way as to provide discrete temporal "batches". This arrangement may facilitate the PCA and PLS decomposition of the data into predictive models. These models may then be applied to an individual's data, to create a prediction of healthcare related events.