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The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States
BACKGROUND: Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM),...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366979/ https://www.ncbi.nlm.nih.gov/pubmed/37496638 http://dx.doi.org/10.4103/jrms.jrms_602_22 |
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author | Mehrpour, Omid Saeedi, Farhad Abdollahi, Jafar Amirabadizadeh, Alireza Goss, Foster |
author_facet | Mehrpour, Omid Saeedi, Farhad Abdollahi, Jafar Amirabadizadeh, Alireza Goss, Foster |
author_sort | Mehrpour, Omid |
collection | PubMed |
description | BACKGROUND: Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM), logistic regression (LR), and random forest (RF), in the outcome prediction of DPH poisoning. MATERIALS AND METHODS: We used the National Poison Data System database and included all of the human exposures of DPH from January 01, 2017 to December 31, 2017, and excluded those cases with missing information, duplicated cases, and those who reported co-ingestion. Data were split into training and test datasets, and three ML models were compared. We developed confusion matrices for each, and standard performance metrics were calculated. RESULTS: Our study population included 53,761 patients with DPH exposure. The most common reasons for exposure, outcome, chronicity of exposure, and formulation were captured. Our results showed that the average precision-recall area under the curve (AUC) of 0.84. LGBM and RF had the highest performance (average AUC of 0.91), followed by LR (average AUC of 0.90). The specificity of the models was 87.0% in the testing groups. The precision of models was 75.0%. Recall (sensitivity) of models ranged between 73% and 75% with an F1 score of 75.0%. The overall accuracy of LGBM, LR, and RF models in the test dataset was 74.8%, 74.0%, and 75.1%, respectively. In total, just 1.1% of patients (mostly those with major outcomes) received physostigmine. CONCLUSION: Our study demonstrates the application of ML in the prediction of DPH poisoning. |
format | Online Article Text |
id | pubmed-10366979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-103669792023-07-26 The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States Mehrpour, Omid Saeedi, Farhad Abdollahi, Jafar Amirabadizadeh, Alireza Goss, Foster J Res Med Sci Original Article BACKGROUND: Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM), logistic regression (LR), and random forest (RF), in the outcome prediction of DPH poisoning. MATERIALS AND METHODS: We used the National Poison Data System database and included all of the human exposures of DPH from January 01, 2017 to December 31, 2017, and excluded those cases with missing information, duplicated cases, and those who reported co-ingestion. Data were split into training and test datasets, and three ML models were compared. We developed confusion matrices for each, and standard performance metrics were calculated. RESULTS: Our study population included 53,761 patients with DPH exposure. The most common reasons for exposure, outcome, chronicity of exposure, and formulation were captured. Our results showed that the average precision-recall area under the curve (AUC) of 0.84. LGBM and RF had the highest performance (average AUC of 0.91), followed by LR (average AUC of 0.90). The specificity of the models was 87.0% in the testing groups. The precision of models was 75.0%. Recall (sensitivity) of models ranged between 73% and 75% with an F1 score of 75.0%. The overall accuracy of LGBM, LR, and RF models in the test dataset was 74.8%, 74.0%, and 75.1%, respectively. In total, just 1.1% of patients (mostly those with major outcomes) received physostigmine. CONCLUSION: Our study demonstrates the application of ML in the prediction of DPH poisoning. Wolters Kluwer - Medknow 2023-06-12 /pmc/articles/PMC10366979/ /pubmed/37496638 http://dx.doi.org/10.4103/jrms.jrms_602_22 Text en Copyright: © 2023 Journal of Research in Medical Sciences https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Original Article Mehrpour, Omid Saeedi, Farhad Abdollahi, Jafar Amirabadizadeh, Alireza Goss, Foster The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States |
title | The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States |
title_full | The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States |
title_fullStr | The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States |
title_full_unstemmed | The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States |
title_short | The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States |
title_sort | value of machine learning for prognosis prediction of diphenhydramine exposure: national analysis of 50,000 patients in the united states |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366979/ https://www.ncbi.nlm.nih.gov/pubmed/37496638 http://dx.doi.org/10.4103/jrms.jrms_602_22 |
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