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Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing
The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7053743/ https://www.ncbi.nlm.nih.gov/pubmed/32126097 http://dx.doi.org/10.1371/journal.pone.0229331 |
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author | Fernandes, Marta Mendes, Rúben Vieira, Susana M. Leite, Francisca Palos, Carlos Johnson, Alistair Finkelstein, Stan Horng, Steven Celi, Leo Anthony |
author_facet | Fernandes, Marta Mendes, Rúben Vieira, Susana M. Leite, Francisca Palos, Carlos Johnson, Alistair Finkelstein, Stan Horng, Steven Celi, Leo Anthony |
author_sort | Fernandes, Marta |
collection | PubMed |
description | The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage in the stratification of patients and in identifying those with higher risk of ICU admission. Adult patients assigned Manchester Triage System (MTS) or Emergency Severity Index (ESI) 1 to 3 from a Portuguese and a United States Emergency Departments were analyzed. Variables routinely collected at triage were used and natural language processing was applied to the patient chief complaint. Stratified random sampling was applied to split the data in train (70%) and test (30%) sets and 10-fold cross validation was performed for model training. Logistic regression, random forests, and a random undersampling boosting algorithm were used. We compared the performance obtained with the reference model—using only triage priorities—with the models using additional variables. For both hospitals, a logistic regression model achieved higher overall performance, yielding areas under the receiver operating characteristic and precision-recall curves of 0.91 (95% CI 0.90-0.92) and 0.30 (95% CI 0.27-0.33) for the United States hospital and of 0.85 (95% CI 0.83-0.86) and 0.06 (95% CI 0.05-0.07) for the Portuguese hospital. Heart rate, pulse oximetry, respiratory rate and systolic blood pressure were the most important predictors of ICU admission. Compared to the reference models, the models using clinical variables and the chief complaint presented higher recall for patients assigned MTS/ESI 3 and can identify patients assigned MTS/ESI 3 who are at risk for ICU admission. |
format | Online Article Text |
id | pubmed-7053743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70537432020-03-12 Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing Fernandes, Marta Mendes, Rúben Vieira, Susana M. Leite, Francisca Palos, Carlos Johnson, Alistair Finkelstein, Stan Horng, Steven Celi, Leo Anthony PLoS One Research Article The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage in the stratification of patients and in identifying those with higher risk of ICU admission. Adult patients assigned Manchester Triage System (MTS) or Emergency Severity Index (ESI) 1 to 3 from a Portuguese and a United States Emergency Departments were analyzed. Variables routinely collected at triage were used and natural language processing was applied to the patient chief complaint. Stratified random sampling was applied to split the data in train (70%) and test (30%) sets and 10-fold cross validation was performed for model training. Logistic regression, random forests, and a random undersampling boosting algorithm were used. We compared the performance obtained with the reference model—using only triage priorities—with the models using additional variables. For both hospitals, a logistic regression model achieved higher overall performance, yielding areas under the receiver operating characteristic and precision-recall curves of 0.91 (95% CI 0.90-0.92) and 0.30 (95% CI 0.27-0.33) for the United States hospital and of 0.85 (95% CI 0.83-0.86) and 0.06 (95% CI 0.05-0.07) for the Portuguese hospital. Heart rate, pulse oximetry, respiratory rate and systolic blood pressure were the most important predictors of ICU admission. Compared to the reference models, the models using clinical variables and the chief complaint presented higher recall for patients assigned MTS/ESI 3 and can identify patients assigned MTS/ESI 3 who are at risk for ICU admission. Public Library of Science 2020-03-03 /pmc/articles/PMC7053743/ /pubmed/32126097 http://dx.doi.org/10.1371/journal.pone.0229331 Text en © 2020 Fernandes et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fernandes, Marta Mendes, Rúben Vieira, Susana M. Leite, Francisca Palos, Carlos Johnson, Alistair Finkelstein, Stan Horng, Steven Celi, Leo Anthony Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing |
title | Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing |
title_full | Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing |
title_fullStr | Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing |
title_full_unstemmed | Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing |
title_short | Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing |
title_sort | predicting intensive care unit admission among patients presenting to the emergency department using machine learning and natural language processing |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7053743/ https://www.ncbi.nlm.nih.gov/pubmed/32126097 http://dx.doi.org/10.1371/journal.pone.0229331 |
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