High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study.

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作者： Weizhuang,Zhou ^[1] ; Yu En,Chan ^[1] ; Chuan Sheng,Foo ^[1] ; Jingxian,Zhang ^[1] ; Jing Xian,Teo ^[2] ; Sonia,Davila ^[2] ; Weiting,Huang ^[3] ; Jonathan,Yap ^[4] ; Stuart,Cook ^[5] ; Patrick,Tan ^[5] ; Calvin Woon-Loong,Chin ^[6] ; Khung Keong,Yeo ^[4] ; Weng Khong,Lim ^[2] ; Pavitra,Krishnaswamy ^[7]

作者单位： Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore. ^[1] SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Singapore. ^[2] SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Singapore. ^[3] Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, Singapore, Singapore. ^[4] Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore. ^[5] Duke-NUS Medical School, Singapore, Singapore. ^[6] Cancer and Stem Biology Program, Duke-NUS Medical School, Singapore, Singapore. ^[7] Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore. ^[8] Genome Institute of Singapore, Agency for Science Technology and Research (A*STAR), Singapore, Singapore. ^[9]

关键词 cardiometabolic disease digital phenotypes free-living heart rate machine learning polygenic risk scores risk prediction time series analysis wearable device

主题词心血管疾病(Cardiovascular Diseases);队列研究(Cohort Studies);人类(Humans);脂类(Lipids);表型(Phenotype)

DOI 10.2196/34669

PMID 35904853

发布时间 2022-08-17

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