ArticleFrontiers in medicine2022
Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 34 citations in OpenAlex.
- Development of a real-world, therapeutic drug monitoring-informed model to predict teicoplanin daily dose in pediatric intensive care unit patients with bacterial infections.International journal of clinical pharmacy · 2026Article
- Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.International journal of clinical pharmacy · 2026Article
- Ensemble Machine Learning Model for Real-Time Valproic Acid Prediction in Epilepsy Treatment.Pharmacopsychiatry · 2026Article
- An Integrated Population Pharmacokinetic and Machine Learning Model for Predicting Tacrolimus Exposure in Adult Patients with Nephrotic Syndrome.Drug design, development and therapy · 2026Article
- An explainable machine learning framework utilizing ultrasound radiomics for the preoperative differentiation between granulomatous lobular mastitis and breast cancer.Frontiers in oncology · 2026Article
- A Comparison of AI and Population PK Models to Predict the Concentrations of Antiepileptic Drugs Using Therapeutic Drug Monitoring Records.Clinical and translational science · 2025Article
- A Real-Time Plasma Concentration Prediction Model for Voriconazole in Elderly Patients via Machine Learning Combined with Population Pharmacokinetics.Drug design, development and therapy · 2025Article
- Machine learning-based prediction model for teicoplanin plasma concentrations in adults with liver disease using real-world data.Frontiers in pharmacology · 2025Article
- Prediction of vancomycin plasma concentration in elderly patients based on multi-algorithm mining combined with population pharmacokinetics.Scientific reports · 2024Article
- Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study.BMC pediatrics · 2024Article
- Optimal use of β-lactams in neonates: machine learning-based clinical decision support system.EBioMedicine · 2024Article
- Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning.BMC cancer · 2024Article
- Monitoring of the trough concentration of valproic acid in pediatric epilepsy patients: a machine learning-based ensemble model.Frontiers in pharmacology · 2024Article
- Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring.Frontiers in pharmacology · 2024Article
- Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content.Frontiers in plant science · 2024Article
- Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.Frontiers in oncology · 2024Article
- Development of Machine Learning-Based Web System for Estimating Pleural Effusion Using Multi-Frequency Bioelectrical Impedance Analyses.Journal of cardiovascular development and disease · 2023Article
- Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients.BMC medical informatics and decision making · 2022Article
- A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.Frontiers in oncology · 2022Article
- Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example.Frontiers in pharmacology · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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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