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

• Published in: NPJ digital medicine 2023-06, Vol.6 (1), p.105-105, Article 105
• Main 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
• Format: Article
• Language: English
• Subjects: 692/308; 692/700/139; Biomedicine; Biotechnology; Blood cancer; Deep learning; Hematology; Medical diagnosis; Medicine; Medicine & Public Health; Sensors; Wearable computers
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-023-00847-2

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.