Daehr: a discriminant analysis framework for electronic health record data and an application to early detection of mental health disorders

摘要

Xiong et al. present an extension of the linear discriminant analysis (LDA) framework using electronic health record (EHR) data for early disease detection, called Daehr. Two important challenges exist in the conventional LDA model: (a) "it is difficult to train an accurate LDA" when there are few samples available for training; and (b) the data is always heterogeneous with significant noise. To address these issues, Daehr leverages the process of alternating projections with l-penalized sparse matrix estimation and nearest positive-definite matrix approximation to train the LDA model. Daehr is designed to "(1) eliminate the data noise caused by the manual encoding of EHR data" and (2) lower the variance of parameter (covariance matrices) estimation for LDA models when only a few patients' EHRs are available for training.