Wearable sensors enable personalized predictions of clinical laboratory measurements
Open accessAbstract
Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.
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A recent study highlighted the potential of data from wearable sensors to predict laboratory results.
“Additionally, a recent study highlighted the potential of data from wearable sensors to predict laboratory results 35 .”
From Multimodal biomedical AI (2022) · cites this paper as [35]
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- Authors
- Dunn, J., Kidzinski, L., Runge, R., Witt, D., Hicks, J. L., Schüssler-Fiorenza Rose, S. M., Li, X., Bahmani, A., Delp, S. L., Hastie, T., & Snyder, M. P.
- Affiliations
- Duke University (US)
- Stanford Medicine (US)
- Stanford University (US)
- Corresponding author
- Dunn, Jessilyn
- Hastie, Trevor
- Snyder, Michael P.
- ORCID
- Venue
- Nature Medicine
- Published
- 2021-05-24
- Volume
- 27
- Issue
- 6
- Pages
- 1105-1112
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC8293303
- OpenAlex
- W3164369261
- Cited by
- 244
- References
- 52
- Citations by year
- 2026: 37 · 2025: 29 · 2024: 65 · 2023: 50 · 2022: 48 · 2021: 15
- FWCI
- 13.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Whitehead Scholar
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable sensors, consumer wearable devices, vital signs, resting heart rate, body temperature, electrodermal activity, machine learning prediction, random forest, LASSO, home health monitoring
- MeSH terms
- Wearable Electronic Devices, Body Temperature, Galvanic Skin Response, Heart Rate, Humans, Monitoring, Physiologic, Movement, Biosensing Techniques, Vital Signs
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