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Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning

An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated...

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Autores principales: Hu, Qiaozhi, Wang, Hualing, Xu, Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967588/
https://www.ncbi.nlm.nih.gov/pubmed/36836131
http://dx.doi.org/10.3390/jcm12041599
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author Hu, Qiaozhi
Wang, Hualing
Xu, Ting
author_facet Hu, Qiaozhi
Wang, Hualing
Xu, Ting
author_sort Hu, Qiaozhi
collection PubMed
description An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient characteristics, including demographics, admissions, and treatments. Eight algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), Random Forest (RF), and Artificial Neural Network (ANN), were used to establish the prediction model. A total of 782 patients were included, and hepatotoxicity was detected in 35.68% (279/782) of the patients. The Random Forest model with the best predictive capacity was chosen to establish the prediction model (receiver operating characteristic curve 0.97, accuracy 64.33%, precision 50.00%, recall 32.14%, and F1 39.13%). Among the 15 risk factors, the highest score was a body mass index of 0.237, followed by age (0.198), the number of drugs (0.151), and the number of comorbidities (0.144). These factors demonstrated their importance in predicting hepatotoxicity associated with low-dose methotrexate. Using machine learning, this novel study established a predictive model for low-dose methotrexate-related hepatotoxicity. The model can improve medication safety in patients taking methotrexate in clinical practice.
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spelling pubmed-99675882023-02-27 Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning Hu, Qiaozhi Wang, Hualing Xu, Ting J Clin Med Article An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient characteristics, including demographics, admissions, and treatments. Eight algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), Random Forest (RF), and Artificial Neural Network (ANN), were used to establish the prediction model. A total of 782 patients were included, and hepatotoxicity was detected in 35.68% (279/782) of the patients. The Random Forest model with the best predictive capacity was chosen to establish the prediction model (receiver operating characteristic curve 0.97, accuracy 64.33%, precision 50.00%, recall 32.14%, and F1 39.13%). Among the 15 risk factors, the highest score was a body mass index of 0.237, followed by age (0.198), the number of drugs (0.151), and the number of comorbidities (0.144). These factors demonstrated their importance in predicting hepatotoxicity associated with low-dose methotrexate. Using machine learning, this novel study established a predictive model for low-dose methotrexate-related hepatotoxicity. The model can improve medication safety in patients taking methotrexate in clinical practice. MDPI 2023-02-17 /pmc/articles/PMC9967588/ /pubmed/36836131 http://dx.doi.org/10.3390/jcm12041599 Text en © 2023 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
Hu, Qiaozhi
Wang, Hualing
Xu, Ting
Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning
title Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning
title_full Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning
title_fullStr Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning
title_full_unstemmed Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning
title_short Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning
title_sort predicting hepatotoxicity associated with low-dose methotrexate using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967588/
https://www.ncbi.nlm.nih.gov/pubmed/36836131
http://dx.doi.org/10.3390/jcm12041599
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