1. Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach.
- Author
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Vetter, Johannes Simon, Schultebraucks, Katharina, Galatzer-Levy, Isaac, Boeker, Heinz, Brühl, Annette, Seifritz, Erich, and Kleim, Birgit
- Subjects
ANXIETY ,DEPRESSED persons ,HAMILTON Depression Inventory ,HOSPITAL admission & discharge ,MULTIMODAL user interfaces ,MACHINE learning - 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 by SHapley Additive explanation (SHAP) values. The prediction model yielded a multi-class AUC = 0.80 in the hold-out set. The predictive power for the binary classification yielded an AUC = 0.83 (sensitivity =.80, specificity =.77). Most relevant predictors for non-response were insomnia symptoms, younger age, anxiety symptoms, depressed mood, being unemployed, suicidal ideation and somatic symptoms of depressive disorder. Non-responders to routine treatment for depression can be identified and screened for potential next-generation treatments. Such predictors may help personalize treatment and improve treatment response. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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