An interesting study from the Stanford University School of Medicine was published in June 2021. In this study, vital signs, including continuous heart rate and body temperature electrodermal activity and movement were used in detecting medical conditions using machine learning. Medical conditions were clinical laboratory test results of several measurements related to electrolytes, diabetes, cardiovascular diseases and immune systems. Bases on results authors concluded that vital signs collected from wearables give more consistent and precise description of resting HR than do measurements taken at clinic. Data collected also predicted several clinical laboratory measurements with lower prediction error that predictions made using clinically obtained vital signs measurements. As a conclusion results also demonstrated that value of wearables for continuous and longitudinal assessments of physiological measurements that today can be measured only with laboratory measurements.
Dunn, J., Kidzinski, L., Runge, R. et al. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nat Med 27, 1105–1112 (2021). https://doi.org/10.1038/s41591-021-01339-0
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