Predicting healthcare associated infections using patients' experiences

Michael A. Pratt; Henry Chu

doi:10.1117/12.2228618

19 May 2016 Predicting healthcare associated infections using patients' experiences

Michael A. Pratt, Henry Chu

Proceedings Volume 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016; 987107 (2016) https://doi.org/10.1117/12.2228618
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Michael A. Pratt and Henry Chu "Predicting healthcare associated infections using patients' experiences", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 987107 (19 May 2016); https://doi.org/10.1117/12.2228618

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