# name
Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number

# metadata.dc.description.abstract[].value
Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

# metadata.dc.relation.ispartof[].value
Translational Psychiatry, 2024, vol. 14, num.1

# metadata.dc.rights[].value
cc-by-nc-nd (c)  Corponi, F. et al., 2024

# metadata.dc.source[].value
Articles publicats en revistes (Medicina)

# metadata.dc.title[].value
Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number