Predictive biosignature of major depressive disorder derived from physiological measurements of outpatients using machine learning.

ABSTRACT: Major Depressive Disorder (MDD) has heterogeneous manifestations, leading to difficulties in predicting the evolution of the disease and in patient's follow-up. We aimed to develop a machine learning algorithm that identifies a biosignature to provide a clinical score of depressive symptoms using individual physiological data. We performed a prospective, multicenter clinical trial where outpatients diagnosed with MDD were enrolled and wore a passive monitoring device constantly for 6 months. A total of 101 physiological measures related to physical activity, heart rate, heart rate variability, breathing rate, and sleep were acquired. For each patient, the algorithm was trained on daily physiological features over the first 3 months as well as corresponding standardized clinical evaluations

SUBMITTER: Ricka N 

PROVIDER: S-EPMC10130089 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature