Moving beyond regression techniques in cardiovascular risk prediction: Applying machine learning to address analytic challenges

European Heart Journal

Volumn 38, Issue 23, 2017, Pages 1805-1814

  Goldstein, Benjamin A ^a,b   Navar, Ann Marie ^b   Carter, Rickey E ^c  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b DUKE CLINICAL RESEARCH INSTITUTE   (United States)

^c MAYO CLINIC   (United States)

Author keywords

Electronic health records; Personalized medicine; Precision medicine; Risk prediction

Indexed keywords

ACUTE HEART INFARCTION; ALGORITHM; CARDIOVASCULAR RISK; CLASSIFICATION; ELECTRONIC HEALTH RECORD; HOSPITAL PATIENT; HUMAN; LABORATORY TEST; MACHINE LEARNING; PARAMETERS; PERSONALIZED MEDICINE; PREDICTOR VARIABLE; PRIORITY JOURNAL; REGRESSION ANALYSIS; REVIEW; RISK ASSESSMENT; BIOLOGICAL MODEL; CARDIOVASCULAR DISEASE; RISK FACTOR; STATISTICS AND NUMERICAL DATA;

CARDIOVASCULAR DISEASES; HUMANS; MACHINE LEARNING; MODELS, BIOLOGICAL; PRECISION MEDICINE; REGRESSION ANALYSIS; RISK ASSESSMENT; RISK FACTORS;

EID: 85021953748     PISSN: 0195668X     EISSN: 15229645     Source Type: Journal    
DOI: 10.1093/eurheartj/ehw302     Document Type: Review

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