Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

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).

[img]
Preview
PDF
Published Version
Available under License : See the attached license file.

Download (619kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

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.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Wu, C
Rosenfeld, R
Clermont, Gcler@pitt.eduCLER0000-0002-0163-1379
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: 14 Mar 2021 10:55
URI: http://d-scholarship.pitt.edu/id/eprint/21969

Metrics

Monthly Views for the past 3 years

Plum Analytics

Altmetric.com


Actions (login required)

View Item View Item