Using data-driven rules to predict mortality in severe community acquired pneumonia.

Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to us...

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Autores principales:	Chuang Wu, Roni Rosenfeld, Gilles Clermont
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2581badbb4814a8f8f435bc1f2ca47e0
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id	oai:doaj.org-article:2581badbb4814a8f8f435bc1f2ca47e0
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spelling	oai:doaj.org-article:2581badbb4814a8f8f435bc1f2ca47e02021-11-18T08:25:10ZUsing data-driven rules to predict mortality in severe community acquired pneumonia.1932-620310.1371/journal.pone.0089053https://doaj.org/article/2581badbb4814a8f8f435bc1f2ca47e02014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24699007/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available.Chuang WuRoni RosenfeldGilles ClermontPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e89053 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chuang Wu Roni Rosenfeld Gilles Clermont Using data-driven rules to predict mortality in severe community acquired pneumonia.
description	Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available.
format	article
author	Chuang Wu Roni Rosenfeld Gilles Clermont
author_facet	Chuang Wu Roni Rosenfeld Gilles Clermont
author_sort	Chuang Wu
title	Using data-driven rules to predict mortality in severe community acquired pneumonia.
title_short	Using data-driven rules to predict mortality in severe community acquired pneumonia.
title_full	Using data-driven rules to predict mortality in severe community acquired pneumonia.
title_fullStr	Using data-driven rules to predict mortality in severe community acquired pneumonia.
title_full_unstemmed	Using data-driven rules to predict mortality in severe community acquired pneumonia.
title_sort	using data-driven rules to predict mortality in severe community acquired pneumonia.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/2581badbb4814a8f8f435bc1f2ca47e0
work_keys_str_mv	AT chuangwu usingdatadrivenrulestopredictmortalityinseverecommunityacquiredpneumonia AT ronirosenfeld usingdatadrivenrulestopredictmortalityinseverecommunityacquiredpneumonia AT gillesclermont usingdatadrivenrulestopredictmortalityinseverecommunityacquiredpneumonia
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Using data-driven rules to predict mortality in severe community acquired pneumonia.

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