近日，美国斯坦福大学医学院Michael P. Snyder等研究人员合作发现，可穿戴式传感器可对临床实验室级测量进行个性化预测。2021年5月24日，《自然—医学》杂志在线发表了这项成果。

研究人员测试了通过消费者可穿戴设备测量的生命体征（即，连续监测的心率、体温、皮肤电活动和运动）是否可以使用机器学习模型（包括随机森林和套索模型）预测临床实验室测试结果。结果表明，与在临床上进行的测量相比，从可穿戴设备收集的生命体征数据可以更一致、更准确地描述静息心率。从可穿戴设备收集的生命体征数据还可以预测几种临床实验室测量值，其预测误差要小于使用临床获得的生命体征测量值所作的预测。

监测生命体征的时间长度以及监测期与预测日期的接近程度在机器学习模型的性能中起着至关重要的作用。这些结果证明了商用可穿戴设备对生理测量值进行连续和纵向评估的价值，目前，这些测量值只能通过临床实验室测试来测量。

据悉，生命体征，包括心率和体温，可用于检测或监测医疗状况，但通常在临床中进行测量，并且需要后续实验室测试以进行更明确的诊断。

附：英文原文

Title: Wearable sensors enable personalized predictions of clinical laboratory measurements

Author: Jessilyn Dunn, Lukasz Kidzinski, Ryan Runge, Daniel Witt, Jennifer L. Hicks, Sophia Miryam Schssler-Fiorenza Rose, Xiao Li, Amir Bahmani, Scott L. Delp, Trevor Hastie, Michael P. Snyder

Issue&Volume: 2021-05-24

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. Data from wearable sensors, including heart rate, body temperature, electrodermal activity and movement, can predict clinical laboratory measurements, with highest accuracy for hematological tests such as hematocrit.

DOI: 10.1038/s41591-021-01339-0

Source: https://www.nature.com/articles/s41591-021-01339-0