Wearable sensors enable personalized predictions of clinical laboratory measurements
Top Cited Papers
- 24 May 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Medicine
- Vol. 27 (6), 1105-1112
- https://doi.org/10.1038/s41591-021-01339-0
Abstract
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.Keywords
Funding Information
- Whitehead Scholar
This publication has 50 references indexed in Scilit:
- UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old AgePLoS Medicine, 2015
- Heart rate and blood pressure: any possible implications for management of hypertension?Current Hypertension Reports, 2012
- Personal Omics Profiling Reveals Dynamic Molecular and Medical PhenotypesCell, 2012
- Sparse canonical correlation analysisMachine Learning, 2010
- A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysisBiostatistics, 2009
- The role of patients and providers in the timing of follow-up visitsJournal of General Internal Medicine, 1999
- Prediction of Coronary Heart Disease Using Risk Factor CategoriesJournal of the American College of Cardiology, 1998
- DehydrationJAMA, 1995
- Dehydration. Evaluation and management in older adults. Council on Scientific Affairs, American Medical AssociationJAMA, 1995
- A Primer on the Precision and Accuracy of the Clinical ExaminationJAMA, 1992