Improving health care organizational management through neural network learning

Ernest Goss, George S. Vozikis

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

In order to provide more ethical and objective measures of the likelihood of Intensive Care Unit (ICU) recovery, hospitals have turned increasingly to decision support system software packages, such as APACHE. However, these packages derive estimates from parametric techniques, such as Binary Logit Regression (BLR) in the APACHE case, and require the developer to specify in advance the functional relationships among variables in the model. Recent rapid advancements in computer software and hardware technology have encouraged researchers to use more computationally intensive, non-parametric techniques such as Neural Networks (NNs), which are purported to be better than parametric models in terms of prediction capabilities. The present study applies both methodologies to a sample of ICU patients and shows that the NN technique predicts mortality rates more correctly than BLR, and offers a promising non-parametric alternative to the parametric methodologies in hospital settings.

Original languageEnglish
Pages (from-to)221-227
Number of pages7
JournalHealth Care Management Science
Volume5
Issue number3
DOIs
StatePublished - 2002

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APACHE
Intensive Care Units
Software
Learning
Delivery of Health Care
Research Personnel
Technology
Mortality
Healthcare
Network learning
Neural networks
Organizational management
Methodology
Intensive care unit
Logit regression

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Health Professions(all)

Cite this

Improving health care organizational management through neural network learning. / Goss, Ernest; Vozikis, George S.

In: Health Care Management Science, Vol. 5, No. 3, 2002, p. 221-227.

Research output: Contribution to journalArticle

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