Comparing two machine learning approaches in predicting lupus hospitalization using longitudinal data.

ABSTRACT: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease characterized by flares ranging from mild to life-threatening. Severe flares and complications can require hospitalizations, which account for most of the direct costs of SLE care. This study investigates two machine learning approaches in predicting SLE hospitalizations using longitudinal data from 925 patients enrolled in a multicenter electronic health record (EHR)-based lupus cohort. Our first Differential approach accounts for the time dependencies in sequential data by introducing additional lagged variables between consecutive time steps. We next evaluate the performance of LSTM, a state-of-the-art deep learning model designed for time series. Our experimental results demonstrate that both methods can effective

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC9525268 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature