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Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies.
- Source :
- NPJ Digital Medicine; 6/2/2023, Vol. 6 Issue 1, p1-9, 9p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 23986352
- Volume :
- 6
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Digital Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 164080387
- Full Text :
- https://doi.org/10.1038/s41746-023-00847-2