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research
Using data-driven rules to predict mortality in severe community acquired pneumonia
Authors
A Ortqvist
AD Sevin
+52 more
C Kooperberg
C Kooperberg
Chuang Wu
D Wang
DC Angus
DC Angus
DM Agnese
E Triantaphyllou
EA Panacek
EB Keeler
EE Vasilevskis
F Stuber
G Clermont
G Clermont
G Clermont
G DiRienzo1
Gilles Clermont
GR Bernard
J Daley
J Kasal
JA Kellum
JE Zimmerman
JL Vincent
Jorge I. F. Salluh
JR Quinlan
K Wang
M Feldmann
M Fine
MJ Fine
MJ Fine
MJ Fine
MM Levy
MT Keegan
N Beerenwinkel
N Beerenwinkel
NY Kurashi
P Clark
RC Read
RG Wunderink
Roni Rosenfeld
RP Dellinger
S Draghici
S Lemeshow
S Leteurtre
SE Poynter
SN Sanchez
SY Rhee
TJ Marrie
U Ruckert
V Kaplan
VTC Silva
Y Sakr
Publication date
1 January 2014
Publisher
'Public Library of Science (PLoS)'
Doi
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on
PubMed
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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