19 May 2016 Predicting healthcare associated infections using patients' experiences
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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.
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Michael A. Pratt, 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); doi: 10.1117/12.2228618; https://doi.org/10.1117/12.2228618
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