Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning
Name
Horng-2017-Creating an automated trigger for s.pdf
Size
2.09 MB
Format
Adobe PDF
Checksum (MD5)
ee35eabdf047025c186a82afeb0ffeab
Author(s)
Horng, Steven
Halpern, Yoni
Jernite, Yacine
Shapiro, Nathan I.
Nathanson, Larry A.
Sontag, David Alexander
Date Issued
April 2017
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Horng, Steven; Sontag, David A.; Halpern, Yoni; Jernite, Yacine; Shapiro, Nathan I. and Nathanson, Larry A. “Creating an Automated Trigger for Sepsis Clinical Decision Support at Emergency Department Triage Using Machine Learning.” Edited by Tudor Groza. PLOS ONE 12, no. 4 (April 2017): e0174708 © 2017 Horng et al
Version
Final published version
Abstract
Objective
To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.
Methods
This was a retrospective, observational cohort study performed at a tertiary academic teaching hospital. All consecutive ED patient visits between 12/17/08 and 2/17/13 were included. No patients were excluded. The primary outcome measure was infection diagnosed in the emergency department defined as a patient having an infection related ED ICD-9-CM discharge diagnosis. Patients were randomly allocated to train (64%), validate (20%), and test (16%) data sets. After preprocessing the free text using bigram and negation detection, we built four models to predict infection, incrementally adding vital signs, chief complaint, and free text nursing assessment. We used two different methods to represent free text: a bag of words model and a topic model. We then used a support vector machine to build the prediction model. We calculated the area under the receiver operating characteristic curve to compare the discriminatory power of each model.
Results
A total of 230,936 patient visits were included in the study. Approximately 14% of patients had the primary outcome of diagnosed infection. The area under the ROC curve (AUC) for the vitals model, which used only vital signs and demographic data, was 0.67 for the training data set, 0.67 for the validation data set, and 0.67 (95% CI 0.65–0.69) for the test data set. The AUC for the chief complaint model which also included demographic and vital sign data was 0.84 for the training data set, 0.83 for the validation data set, and 0.83 (95% CI 0.81–0.84) for the test data set. The best performing methods made use of all of the free text. In particular, the AUC for the bag-of-words model was 0.89 for training data set, 0.86 for the validation data set, and 0.86 (95% CI 0.85–0.87) for the test data set. The AUC for the topic model was 0.86 for the training data set, 0.86 for the validation data set, and 0.85 (95% CI 0.84–0.86) for the test data set.
Conclusion
Compared to previous work that only used structured data such as vital signs and demographic information, utilizing free text drastically improves the discriminatory ability (increase in AUC from 0.67 to 0.86) of identifying infection.
MIT Department
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0174708
