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

Frontiers in Medicine

article

Pan Ma¹  Ruixiang Liu¹  Wenrui Gu¹  Qing Dai¹  Yu Gan¹  Jing Cen¹  Shenglan Shang²  Fang Liu¹  Yongchuan Chen¹ 

[1] Department of Pharmacy, The First Affiliated Hospital of Third Military Medical University ,(Army Medical University);Department of Clinical Pharmacy, General Hospital of Central Theater Command of PLA

关键词: machine learning;    SHAP;    precision medicine;    prediction model;    model explanation;    algorithm;    teicoplanin;   

DOI  :  10.3389/fmed.2022.808969

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY