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- Title
Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies.
- Authors
Jacobsen, Malte; Gholamipoor, Rahil; Dembek, Till A.; Rottmann, Pauline; Verket, Marlo; Brandts, Julia; Jäger, Paul; Baermann, Ben-Niklas; Kondakci, Mustafa; Heinemann, Lutz; Gerke, Anna L.; Marx, Nikolaus; Müller-Wieland, Dirk; Möllenhoff, Kathrin; Seyfarth, Melchior; Kollmann, Markus; Kobbe, Guido
- 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 regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at −24 h and 0.88 at −48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.
- Subjects
DEEP learning; RESEARCH; SCIENTIFIC observation; SAMPLE size (Statistics); SELF-management (Psychology); VITAL signs; ELECTRONIC equipment; ETHICS committees; PHYSICAL activity; COMPARATIVE studies; HEMATOLOGIC malignancies; AT-risk people; RESEARCH funding; SENSITIVITY &; specificity (Statistics); STATISTICAL sampling; EARLY diagnosis; LONGITUDINAL method
- Publication
NPJ Digital Medicine, 2023, Vol 6, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-023-00847-2