Predicting outcomes of hospitalization for heart failure using logistic regression and knowledge discovery methods.
The purpose of this study is to determine the best prediction of heart failure outcomes, resulting from two methods -- standard epidemiologic analysis with logistic regression and knowledge discovery with supervised learning/data mining. Heart failure was chosen for this study as it exhibits higher prevalence and cost of treatment than most other hospitalized diseases. The prevalence of heart failure has exceeded 4 million cases in the U.S.. Findings of this study should be useful for the design of quality improvement initiatives, as particular aspects of patient comorbidity and treatment are found to be associated with mortality. This is also a proof of concept study, considering the feasibility of emerging health informatics methods of data mining in conjunction with or in lieu of traditional logistic regression methods of prediction. Findings may also support the design of decision support systems and quality improvement programming for other diseases.
Phillips
Kirk T
KT
Iowa Health System, Des Moines, Iowa, USA.
Street
W Nick
WN
D019992:Databases as Topic; D003663:Decision Trees; D005240:Feasibility Studies; D006333:Heart Failure; D006760:Hospitalization; D006801:Humans; D016247:Information Storage and Retrieval; D016015:Logistic Models; D016571:Neural Networks, Computer; D011379:Prognosis; D012372:ROC Curve