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

Wu, C and Rosenfeld, R and Clermont, G (2014) Using data-driven rules to predict mortality in severe community acquired pneumonia. PLoS ONE, 9 (4).

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Abstract

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. © 2014 Wu et al.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Wu, C
Rosenfeld, R
Clermont, Gcler@pitt.eduCLER
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
EditorSalluh, Jorge I. F.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: 3 April 2014
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 9
Number: 4
DOI or Unique Handle: 10.1371/journal.pone.0089053
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
School of Medicine > Critical Care Medicine
Swanson School of Engineering > Industrial Engineering
Refereed: Yes
Date Deposited: 23 Jun 2014 21:08
Last Modified: 07 Dec 2019 15:55
URI: http://d-scholarship.pitt.edu/id/eprint/21969

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