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Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach
BACKGROUND: Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. OBJECTIVE: The purpose of this work was to c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131621/ https://www.ncbi.nlm.nih.gov/pubmed/36943366 http://dx.doi.org/10.2196/44666 |
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author | Liu, Yuxuan Lyu, Xiaoguang Yang, Bo Fang, Zhixiang Hu, Dejun Shi, Lei Wu, Bisheng Tian, Yong Zhang, Enli Yang, YuanChao |
author_facet | Liu, Yuxuan Lyu, Xiaoguang Yang, Bo Fang, Zhixiang Hu, Dejun Shi, Lei Wu, Bisheng Tian, Yong Zhang, Enli Yang, YuanChao |
author_sort | Liu, Yuxuan |
collection | PubMed |
description | BACKGROUND: Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. OBJECTIVE: The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies. METHODS: In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations. RESULTS: Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians’ assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73). CONCLUSIONS: The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes. |
format | Online Article Text |
id | pubmed-10131621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101316212023-04-27 Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach Liu, Yuxuan Lyu, Xiaoguang Yang, Bo Fang, Zhixiang Hu, Dejun Shi, Lei Wu, Bisheng Tian, Yong Zhang, Enli Yang, YuanChao JMIR Form Res Original Paper BACKGROUND: Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. OBJECTIVE: The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies. METHODS: In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations. RESULTS: Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians’ assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73). CONCLUSIONS: The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes. JMIR Publications 2023-03-21 /pmc/articles/PMC10131621/ /pubmed/36943366 http://dx.doi.org/10.2196/44666 Text en ©Yuxuan Liu, Xiaoguang Lyu, Bo Yang, Zhixiang Fang, Dejun Hu, Lei Shi, Bisheng Wu, Yong Tian, Enli Zhang, YuanChao Yang. Originally published in JMIR Formative Research (https://formative.jmir.org), 21.03.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Liu, Yuxuan Lyu, Xiaoguang Yang, Bo Fang, Zhixiang Hu, Dejun Shi, Lei Wu, Bisheng Tian, Yong Zhang, Enli Yang, YuanChao Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach |
title | Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach |
title_full | Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach |
title_fullStr | Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach |
title_full_unstemmed | Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach |
title_short | Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach |
title_sort | early triage of critically ill adult patients with mushroom poisoning: machine learning approach |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131621/ https://www.ncbi.nlm.nih.gov/pubmed/36943366 http://dx.doi.org/10.2196/44666 |
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