Big data solutions for predicting risk-of-readmission for congestive heart failure patients

Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Volumn , Issue , 2013, Pages 64-71

  Zolfaghar, Kiyana ^a   Meadem, Naren ^a   Teredesai, Ankur ^a   Roy, Senjuti Basu ^a   Chin, Si Chi ^a   Muckian, Brian ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Multicare Health System   (United States)

Author keywords

Healthcare; Knowledge Discovery; Risk Prediction

Indexed keywords

BIG DATA; CARDIOLOGY; FORECASTING; HEALTH CARE; HEALTH RISKS; HOSPITALS; PREDICTIVE ANALYTICS;

CONGESTIVE HEART FAILURES; DISTRIBUTED AND PARALLEL COMPUTING; EFFECTIVENESS AND EFFICIENCIES; HEALTH CARE INFORMATICS; HEALTH CARE PROVIDERS; PREDICTIVE MODELING; RISK PREDICTIONS; SOCIO-DEMOGRAPHIC FACTORS;

DATA MINING;

EID: 84893330267     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.2013.6691760     Document Type: Conference Paper

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