Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study

Abstract Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, th...

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Autores principales:	Naoya Kobayashi, Takuya Shiga, Saori Ikumi, Kazuki Watanabe, Hitoshi Murakami, Masanori Yamauchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cc04a7db7f794b2081fe7c26f39d3738
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spelling	oai:doaj.org-article:cc04a7db7f794b2081fe7c26f39d37382021-12-02T11:35:40ZSemi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study10.1038/s41598-021-84714-82045-2322https://doaj.org/article/cc04a7db7f794b2081fe7c26f39d37382021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84714-8https://doaj.org/toc/2045-2322Abstract Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were performed in eligible patients using the Critical-Care Pain Observation Tool (CPOT). Three machine-learning methods—random forest (RF), support vector machine (SVM), and logistic regression (LR)—were employed to predict pain using parameters, such as vital signs, age group, and sedation levels. Prediction accuracy was calculated as the harmonic mean of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Furthermore, 117,190 CPOT assessments were performed in 11,507 eligible patients (median age: 65 years; 58.0% males). We found that pain prediction was possible with all three machine-learning methods. RF demonstrated the highest AUROC for the test data (RF: 0.853, SVM: 0.823, and LR: 0.787). With this method, pain can be objectively, continuously, and semi-automatically evaluated in critically ill patients.Naoya KobayashiTakuya ShigaSaori IkumiKazuki WatanabeHitoshi MurakamiMasanori YamauchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naoya Kobayashi Takuya Shiga Saori Ikumi Kazuki Watanabe Hitoshi Murakami Masanori Yamauchi Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study
description	Abstract Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were performed in eligible patients using the Critical-Care Pain Observation Tool (CPOT). Three machine-learning methods—random forest (RF), support vector machine (SVM), and logistic regression (LR)—were employed to predict pain using parameters, such as vital signs, age group, and sedation levels. Prediction accuracy was calculated as the harmonic mean of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Furthermore, 117,190 CPOT assessments were performed in 11,507 eligible patients (median age: 65 years; 58.0% males). We found that pain prediction was possible with all three machine-learning methods. RF demonstrated the highest AUROC for the test data (RF: 0.853, SVM: 0.823, and LR: 0.787). With this method, pain can be objectively, continuously, and semi-automatically evaluated in critically ill patients.
format	article
author	Naoya Kobayashi Takuya Shiga Saori Ikumi Kazuki Watanabe Hitoshi Murakami Masanori Yamauchi
author_facet	Naoya Kobayashi Takuya Shiga Saori Ikumi Kazuki Watanabe Hitoshi Murakami Masanori Yamauchi
author_sort	Naoya Kobayashi
title	Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study
title_short	Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study
title_full	Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study
title_fullStr	Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study
title_full_unstemmed	Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study
title_sort	semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cc04a7db7f794b2081fe7c26f39d3738
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Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study

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