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Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients
Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propos...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8465577/ https://www.ncbi.nlm.nih.gov/pubmed/34575711 http://dx.doi.org/10.3390/jpm11090934 |
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author | Na Pattalung, Thanakron Ingviya, Thammasin Chaichulee, Sitthichok |
author_facet | Na Pattalung, Thanakron Ingviya, Thammasin Chaichulee, Sitthichok |
author_sort | Na Pattalung, Thanakron |
collection | PubMed |
description | Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient’s mortality in the intensive care unit (ICU). We investigated our approach on three public critical care databases: Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III), MIMIC-IV, and eICU. Our models achieved an area under the receiver operating characteristic curve (AUC) of 0.87–0.91. Our approach was not only able to provide the predicted mortality risk but also to recognize and explain the historical contributions of the associated factors to the prediction. The explanations provided by our model were consistent with the literature. Patients may benefit from early intervention if their clinical observations in the ICU are continuously monitored in real time. |
format | Online Article Text |
id | pubmed-8465577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84655772021-09-27 Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients Na Pattalung, Thanakron Ingviya, Thammasin Chaichulee, Sitthichok J Pers Med Article Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient’s mortality in the intensive care unit (ICU). We investigated our approach on three public critical care databases: Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III), MIMIC-IV, and eICU. Our models achieved an area under the receiver operating characteristic curve (AUC) of 0.87–0.91. Our approach was not only able to provide the predicted mortality risk but also to recognize and explain the historical contributions of the associated factors to the prediction. The explanations provided by our model were consistent with the literature. Patients may benefit from early intervention if their clinical observations in the ICU are continuously monitored in real time. MDPI 2021-09-19 /pmc/articles/PMC8465577/ /pubmed/34575711 http://dx.doi.org/10.3390/jpm11090934 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Na Pattalung, Thanakron Ingviya, Thammasin Chaichulee, Sitthichok Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients |
title | Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients |
title_full | Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients |
title_fullStr | Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients |
title_full_unstemmed | Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients |
title_short | Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients |
title_sort | feature explanations in recurrent neural networks for predicting risk of mortality in intensive care patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8465577/ https://www.ncbi.nlm.nih.gov/pubmed/34575711 http://dx.doi.org/10.3390/jpm11090934 |
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