Journal article
Predicting outcomes of hospitalization for heart failure using logistic regression and knowledge discovery methods
AMIA ... Annual Symposium proceedings, Vol.2005, pp.1080-1080
2005
PMCID: PMC1560853
PMID: 16779367
Abstract
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.
Details
- Title: Subtitle
- Predicting outcomes of hospitalization for heart failure using logistic regression and knowledge discovery methods
- Creators
- Kirk T Phillips - UnityPoint HealthW Nick Street
- Resource Type
- Journal article
- Publication Details
- AMIA ... Annual Symposium proceedings, Vol.2005, pp.1080-1080
- PMID
- 16779367
- PMCID
- PMC1560853
- ISSN
- 1559-4076
- eISSN
- 1942-597X
- Language
- English
- Date published
- 2005
- Academic Unit
- Bus Admin College; Health Management and Policy; Nursing; Computer Science; Business Analytics; Medicine Administration
- Record Identifier
- 9984380520202771
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