A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data

Statistics in Medicine

Volumn 34, Issue 21, 2015, Pages 2941-2957

  Wolfson, Julian ^a   Bandyopadhyay, Sunayan ^b   Elidrisi, Mohamed ^b   Vazquez Benitez, Gabriela ^d   Vock, David M ^a   Musgrove, Donald ^a   Adomavicius, Gediminas ^c   Johnson, Paul E ^c   O'Connor, Patrick J ^d  

^a UNIVERSITY OF MINNESOTA   (United States)

^b University of Minnesota   (United States)

^c UNIVERSITY OF MINNESOTA   (United States)

^d HEALTHPARTNERS INSTITUTE   (United States)

Author keywords

Electronic health records; Machine learning; Naive Bayes; Risk prediction; Survival analysis

Indexed keywords

ARTICLE; CALIBRATION; CARDIOVASCULAR RISK; CENSORED NAIVE BAYES MACHINE LEARNING; CLASSIFICATION ALGORITHM; HEALTH CARE SYSTEM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MACHINE LEARNING; MATHEMATICAL COMPUTING; OUTCOME ASSESSMENT; PREDICTION; PROCESS DEVELOPMENT; PROPORTIONAL HAZARDS MODEL; RISK ASSESSMENT; STATISTICAL DISTRIBUTION; STATISTICAL SIGNIFICANCE; TIME TO EVENT DATA; BAYES THEOREM; BIOMETRY; CARDIOVASCULAR DISEASES; COMPUTER SIMULATION; ELECTRONIC HEALTH RECORD; FACTUAL DATABASE; HUMAN; INTEGRATED HEALTH CARE SYSTEM; LONGITUDINAL STUDY; PROCEDURES; RISK; SPATIOTEMPORAL ANALYSIS; UNITED STATES;

BAYES THEOREM; BIOMETRY; CARDIOVASCULAR DISEASES; COMPUTER SIMULATION; DATABASES, FACTUAL; DELIVERY OF HEALTH CARE, INTEGRATED; ELECTRONIC HEALTH RECORDS; HUMANS; LONGITUDINAL STUDIES; MACHINE LEARNING; MIDWESTERN UNITED STATES; PROPORTIONAL HAZARDS MODELS; RISK; RISK ASSESSMENT; SPACE-TIME CLUSTERING;

EID: 84938292482     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6526     Document Type: Article

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