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Proceedings Paper

Predicting healthcare associated infections using patients' experiences
Author(s): Michael A. Pratt; Henry Chu
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Paper Abstract

Healthcare associated infections (HAI) are a major threat to patient safety and are costly to health systems. Our goal is to predict the HAI performance of a hospital using the patients' experience responses as input. We use four classifiers, viz. random forest, naive Bayes, artificial feedforward neural networks, and the support vector machine, to perform the prediction of six types of HAI. The six types include blood stream, urinary tract, surgical site, and intestinal infections. Experiments show that the random forest and support vector machine perform well across the six types of HAI.

Paper Details

Date Published: 19 May 2016
PDF: 8 pages
Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 987107 (19 May 2016); doi: 10.1117/12.2228618
Show Author Affiliations
Michael A. Pratt, Univ. of Louisiana at Lafayette (United States)
Henry Chu, Univ. of Louisiana at Lafayette (United States)


Published in SPIE Proceedings Vol. 9871:
Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
Liyi Dai; Yufeng Zheng; Henry Chu; Anke D. Meyer-Bäse, Editor(s)

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