Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: Comparison of machine learning and other statistical approaches

JAMA Cardiology

Volumn 2, Issue 2, 2017, Pages 204-209

  Frizzell, Jarrod D ^a   Liang, Li ^b   Schulte, Phillip J ^c   Yancy, Clyde W ^d   Heidenreich, Paul A ^e   Hernandez, Adrian F ^b   Bhatt, Deepak L ^f   Fonarow, Gregg C ^g   Laskey, Warren K ^a  

^a UNIVERSITY OF NEW MEXICO SCHOOL OF MEDICINE   (United States)

^b DUKE CLINICAL RESEARCH INSTITUTE   (United States)

^c MAYO CLINIC   (United States)

^d NORTHWESTERN UNIVERSITY   (United States)

^e STANFORD UNIVERSITY   (United States)

^f BRIGHAM AND WOMEN S HOSPITAL   (United States)

^g UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

30 DAY ALL CAUSE READMISSION; ALGORITHM; ARTICLE; BAYES THEOREM; CALIBRATION; CONTROLLED STUDY; CONVENTIONAL STATISTICS BASED METHOD; ELECTRONIC HEALTH RECORD; GRADIENT BOOSTED MODEL; HEART FAILURE; HOSPITAL DISCHARGE; HOSPITAL READMISSION; HUMAN; INTERMETHOD COMPARISON; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR MODEL; LOGISTIC REGRESSION ANALYSIS; LOGISTIC REGRESSION MODEL; MACHINE LEARNING; MAJOR CLINICAL STUDY; MEDICARE; PRACTICE GUIDELINE; PREDICTION; PREDICTIVE VALUE; PRIORITY JOURNAL; RANDOM FOREST; STATISTICAL ANALYSIS; STATISTICAL MODEL; TREE AUGMENTED NAIVE BAYESIAN NETWORK MODEL; VALIDATION PROCESS; CLINICAL TRIAL; COMPARATIVE STUDY; FOLLOW UP; MULTICENTER STUDY; PROGNOSIS; REGISTER; STATISTICS AND NUMERICAL DATA; UNITED STATES;

ALGORITHMS; ELECTRONIC HEALTH RECORDS; FOLLOW-UP STUDIES; HEART FAILURE; HUMANS; MACHINE LEARNING; MODELS, STATISTICAL; PATIENT READMISSION; PREDICTIVE VALUE OF TESTS; PROGNOSIS; REGISTRIES; UNITED STATES;

EID: 85017203403     PISSN: 23806583     EISSN: 23806591     Source Type: Journal    
DOI: 10.1001/jamacardio.2016.3956     Document Type: Article

References (16)

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