Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach.

ABSTRACT: A considerable number of depressed patients do not respond to treatment. Accurate prediction of non-response to routine clinical care may help in treatment planning and improve results. A longitudinal sample of N = 239 depressed patients was assessed at admission to multi-modal day clinic treatment, after six weeks, and at discharge. First, patient's treatment response was modelled by identifying longitudinal trajectories using the Hamilton Depression Rating Scale (HDRS-17). Then, individual items of the HDRS-17 at admission as well as individual patient characteristics were entered as predictors of response/non-response trajectories into the binary classification model (eXtremeGradient Boosting; XGBoost). The model was evaluated on a hold-out set and explained in human-interpretable form

SUBMITTER: Vetter JS 

PROVIDER: S-EPMC8971434 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature