A comparison of models for predicting early hospital readmissions

Journal of Biomedical Informatics

Volumn 56, Issue , 2015, Pages 229-238

  Futoma, Joseph ^a   Morris, Jonathan ^b   Lucas, Joseph ^a,c  

^a Duke University   (United States)

^b IQVIA   (United States)

^c Duke University   (United States)

Author keywords

Deep learning; Early readmission; Electronic health records; Penalized methods; Predictive models; Random forest

Indexed keywords

COST EFFECTIVENESS; DECISION TREES; DEEP LEARNING; HEALTH INSURANCE; HOSPITALS; LEARNING SYSTEMS; RANDOM FORESTS; REGRESSION ANALYSIS; RISK ASSESSMENT; RISK MANAGEMENT;

COMPARISON OF MODELS; EARLY READMISSION; ELECTRONIC HEALTH RECORD; PATIENT POPULATION; PENALIZED METHOD; PREDICTIVE MODELS; PREDICTIVE PERFORMANCE; RISK-SHARING ARRANGEMENT;

PREDICTIVE ANALYTICS;

ADULT; AFRICAN; ALGORITHM; ARTICLE; ASIAN; CAUCASIAN; COHORT ANALYSIS; COST EFFECTIVENESS ANALYSIS; FEMALE; HISPANIC; HOSPITAL ADMISSION; HOSPITAL READMISSION; HUMAN; INTERMETHOD COMPARISON; LENGTH OF STAY; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; AREA UNDER THE CURVE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; COMPUTER PROGRAM; COST BENEFIT ANALYSIS; DIAGNOSIS RELATED GROUP; ECONOMICS; ELECTRONIC HEALTH RECORD; HEALTH SERVICES RESEARCH; HOSPITAL; MIDDLE AGED; NEW ZEALAND; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; REGRESSION ANALYSIS; RISK ASSESSMENT; STATISTICS AND NUMERICAL DATA;

ADULT; ALGORITHMS; AREA UNDER CURVE; COST-BENEFIT ANALYSIS; DIAGNOSIS-RELATED GROUPS; ELECTRONIC HEALTH RECORDS; FEMALE; HEALTH SERVICES RESEARCH; HOSPITALS; HUMANS; LIKELIHOOD FUNCTIONS; MALE; MIDDLE AGED; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NEW ZEALAND; PATIENT READMISSION; REGRESSION ANALYSIS; RISK ASSESSMENT; ROC CURVE; SOFTWARE;

EID: 84938596257     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2015.05.016     Document Type: Article

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