An analytic approach to better understanding and management of coronary surgeries

Dursun Delen, Asil Oztekin, Leman Tomak

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

Demand for high-quality, affordable healthcare services increasing with the aging population in the US. In order to cope with this situation, decision makers in healthcare (managerial, administrative and/or clinical) need to be increasingly more effective and efficient at what they do. Along with expertise, information and knowledge are the other key sources for better decisions. Data mining techniques are becoming a popular tool for extracting information/knowledge hidden deep into large healthcare databases. In this study, using a large, feature-rich, nationwide inpatient databases along with four popular machine learning techniques, we developed predictive models; and using an information fusion based sensitivity analysis on these models, we explained the surgical outcome of a patient undergoing a coronary artery bypass grafting. In this study, support vector machines produced the best prediction results (87.74%) followed by decision trees and neural networks. Studies like this illustrate the fact that accurate prediction and better understanding of such complex medical interventions can potentially lead to more favorable outcomes and optimal use of limited healthcare resources.

Original languageEnglish
Pages (from-to)698-705
Number of pages8
JournalDecision Support Systems
Volume52
Issue number3
DOIs
StatePublished - 1 Feb 2012
Externally publishedYes

Fingerprint

Surgery
Delivery of Health Care
Information fusion
Decision trees
Databases
Sensitivity analysis
Support vector machines
Data mining
Anatomic Models
Learning systems
Decision Trees
Data Mining
Aging of materials
Quality of Health Care
Neural networks
Coronary Artery Bypass
Inpatients
Population
Healthcare
Data base

Keywords

  • Clinical decision support systems
  • Coronary artery bypass surgery (CABG)
  • Data mining
  • Heart disease
  • Machine learning
  • Sensitivity analysis
  • Survival prediction

Cite this

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An analytic approach to better understanding and management of coronary surgeries. / Delen, Dursun; Oztekin, Asil; Tomak, Leman.

In: Decision Support Systems, Vol. 52, No. 3, 01.02.2012, p. 698-705.

Research output: Contribution to journalArticle

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