Using Machine Learning to Identify Health Outcomes from Electronic Health Record Data

被引:0
作者
Jenna Wong
Mara Murray Horwitz
Li Zhou
Sengwee Toh
机构
[1] Harvard Medical School and Harvard Pilgrim Health Care Institute,Department of Population Medicine
[2] Brigham and Women’s Hospital,Division of General Internal Medicine and Primary Care
[3] Harvard Medical School,undefined
来源
Current Epidemiology Reports | 2018年 / 5卷
关键词
Electronic health records; Machine learning; Health outcomes; Phenotyping; Cohort identification;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:331 / 342
页数:11
相关论文
共 287 条
[1]  
Singh S(2012)Drug safety assessment in clinical trials: methodological challenges and opportunities Trials 13 138-78
[2]  
Loke YK(2017)Surrogate endpoints in oncology: when are they acceptable for regulatory and clinical decisions, and are they currently overused? BMC Med 15 134-1039
[3]  
Kemp R(2000)Debate: the slippery slope of surrogate outcomes Curr Control Trials Cardiovasc Med 1 76-1016
[4]  
Prasad V(2017)Good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE special task force on real-world evidence in health care decision making Pharmacoepidemiol Drug Saf 26 1033-93
[5]  
D’Agostino RB(2015)Identifying health outcomes in healthcare databases Pharmacoepidemiol Drug Saf 24 1009-1015
[6]  
Berger ML(2012)Chapter 13: mining electronic health Records in the Genomics era PLoS Comput Biol 8 e1002823-627
[7]  
Sox H(2016)A framework to support the sharing and reuse of computable phenotype definitions across health care delivery and clinical research applications eGEMs 4 1232-1318
[8]  
Willke RJ(2016)Big data and its role in health economics and outcomes research: a collection of perspectives on data sources, measurement, and analysis PharmacoEconomics 34 91-245
[9]  
Brixner DL(2016)Extracting information from the text of electronic medical records to improve case detection: a systematic review J Am Med Inform Assoc 23 1007-90
[10]  
Eichler HG(2017)Classification of breast cancer histology images using convolutional neural networks PLoS One 12 e0177544-243