Forecasting mental states in schizophrenia using digital phenotyping data.

ABSTRACT: The promise of machine learning successfully exploiting digital phenotyping data to forecast mental states in psychiatric populations could greatly improve clinical practice. Previous research focused on binary classification and continuous regression, disregarding the often ordinal nature of prediction targets derived from clinical rating scales. In addition, mental health ratings typically show important class imbalance or skewness that need to be accounted for when evaluating predictive performance. Besides it remains unclear which machine learning algorithm is best suited for forecast tasks, the eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) algorithms being 2  popular choices in digital phenotyping studies. The CrossCheck dataset includes 6,364 mental state surv

SUBMITTER: Jean T 

PROVIDER: S-EPMC11805420 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature