Applying diagnosis support systems in electronic health records to identify wild-type transthyretin amyloid cardiomyopathy risk.

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作者： Connor,Willis ^[1] ; Alexandre H,Watanabe ^[1] ; Justin,Hughes ^[2] ; Kimberly,Nolen ^[3] ; Jason,O'Meara ^[3] ; Alexander,Schepart ^[3] ; Marianna,Bruno ^[3] ; Joseph,Biskupiak ^[1] ; Kensaku,Kawamoto ^[4] ; Nawar,Shara ^[2] ; Thomas,Kannampallil ^[5]

作者单位： Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA. ^[1] Center for Biostatistics, Informatics & Data Science, MedStar Health Research Institute, Hyattsville, MD 20782, USA. ^[2] Medical Affairs, Pfizer, New York, NY 10017, USA. ^[3] Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA. ^[4] Department of Anesthesiology & The Institute for Informatics, Washington University School of Medicine, St Louis, MO 63130, USA. ^[5]

关键词 amyloidosis artificial intelligence cardiomyopathy diagnosis electronic health record heart failure machine learning screening transthyretin

主题词淀粉样神经病, 家族性(Amyloid Neuropathies, Familial);心肌疾病(Cardiomyopathies);电子健康病历(Electronic Health Records);心力衰竭(Heart Failure);人类(Humans);前白蛋白(Prealbumin)

DOI 10.2217/fca-2021-0122

PMID 35098741

发布时间 2022-05-23

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准确一般字义偏差语意偏离译文作用小提交