Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies.

ABSTRACT: Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in

SUBMITTER: Jacobsen M 

PROVIDER: S-EPMC10238496 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature