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Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning

OBJECTIVE: To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method. METHODS: A retrospective study was performed on 279 therapeutic drug monitor...

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Autores principales: Ma, Pan, Liu, Ruixiang, Gu, Wenrui, Dai, Qing, Gan, Yu, Cen, Jing, Shang, Shenglan, Liu, Fang, Chen, Yongchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963816/
https://www.ncbi.nlm.nih.gov/pubmed/35360734
http://dx.doi.org/10.3389/fmed.2022.808969
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author Ma, Pan
Liu, Ruixiang
Gu, Wenrui
Dai, Qing
Gan, Yu
Cen, Jing
Shang, Shenglan
Liu, Fang
Chen, Yongchuan
author_facet Ma, Pan
Liu, Ruixiang
Gu, Wenrui
Dai, Qing
Gan, Yu
Cen, Jing
Shang, Shenglan
Liu, Fang
Chen, Yongchuan
author_sort Ma, Pan
collection PubMed
description OBJECTIVE: To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method. METHODS: A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model. RESULTS: Three algorithms (SVR, GBRT, and RF) with high R(2) scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R(2) = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors. CONCLUSION: We firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance.
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spelling pubmed-89638162022-03-30 Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning Ma, Pan Liu, Ruixiang Gu, Wenrui Dai, Qing Gan, Yu Cen, Jing Shang, Shenglan Liu, Fang Chen, Yongchuan Front Med (Lausanne) Medicine OBJECTIVE: To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method. METHODS: A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model. RESULTS: Three algorithms (SVR, GBRT, and RF) with high R(2) scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R(2) = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors. CONCLUSION: We firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance. Frontiers Media S.A. 2022-03-08 /pmc/articles/PMC8963816/ /pubmed/35360734 http://dx.doi.org/10.3389/fmed.2022.808969 Text en Copyright © 2022 Ma, Liu, Gu, Dai, Gan, Cen, Shang, Liu and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Ma, Pan
Liu, Ruixiang
Gu, Wenrui
Dai, Qing
Gan, Yu
Cen, Jing
Shang, Shenglan
Liu, Fang
Chen, Yongchuan
Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning
title Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning
title_full Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning
title_fullStr Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning
title_full_unstemmed Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning
title_short Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning
title_sort construction and interpretation of prediction model of teicoplanin trough concentration via machine learning
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963816/
https://www.ncbi.nlm.nih.gov/pubmed/35360734
http://dx.doi.org/10.3389/fmed.2022.808969
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