Analyzing 30-day readmission rate for heart failure using different predictive models

Studies in Health Technology and Informatics

Volumn 225, Issue , 2016, Pages 143-147

  Mahajan, Satish ^a,b,c   Burman, Prabir ^d   Hogarth, Michael ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c VA PALO ALTO HEALTH CARE SYSTEM   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Electronic health records; Heart Failure; Logistic regression; Predictive models; Random forest; Readmission

Indexed keywords

CARDIOLOGY; DECISION TREES; DIAGNOSIS; HEALTH INSURANCE; HEALTH RISKS; HOSPITALS; NURSING; PATIENT TREATMENT; RECORDS MANAGEMENT;

ELECTRONIC HEALTH RECORD; HEART FAILURE; LOGISTIC REGRESSIONS; PREDICTIVE MODELS; RANDOM FORESTS; READMISSION;

REGRESSION ANALYSIS;

COMPARATIVE STUDY; ELECTRONIC HEALTH RECORD; EVALUATION STUDY; GERMANY; HEART FAILURE; HOSPITAL READMISSION; HUMAN; INCIDENCE; OUTCOME ASSESSMENT; PROGNOSIS; PROPORTIONAL HAZARDS MODEL; REGRESSION ANALYSIS; REPRODUCIBILITY; RISK FACTOR; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; UNITED STATES;

ELECTRONIC HEALTH RECORDS; GERMANY; HEART FAILURE; HUMANS; INCIDENCE; LOGISTIC MODELS; OUTCOME ASSESSMENT (HEALTH CARE); PATIENT READMISSION; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; RISK FACTORS; SENSITIVITY AND SPECIFICITY; UNITED STATES;

EID: 84978639760     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-61499-658-3-143     Document Type: Conference Paper

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