ArticleFrontiers in oncology2022
Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- 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
- Trastuzumab-TKI combination in HER2-Positive tumors: a FAERS-Based safety profile and multimodal analysis of Tanespimycin's role in enhancing targeting of the HSP90AA1-PI3K-Akt-mTOR axis.Frontiers in pharmacology · 2026Article
- Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach.Scientific reports · 2025Article
- Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review.Clinical pharmacokinetics · 2024Article
- A model for identifying potentially inappropriate medication used in older people with dementia: a machine learning study.International journal of clinical pharmacy · 2024Article
- Article
- Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.Frontiers in pharmacology · 2023Review
- Article
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Authors and funding
11 authors.
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Abstract
Lapatinib is used for the treatment of metastatic HER2(+) breast cancer. We aim to establish a prediction model for lapatinib dose using machine learning and deep learning techniques based on a real-world study. There were 149 breast cancer patients enrolled from July 2016 to June 2017 at Fudan University Shanghai Cancer Center. The sequential forward selection algorithm based on random forest was applied for variable selection. Twelve machine learning and deep learning algorithms were compared in terms of their predictive abilities (logistic regression, SVM, random forest, Adaboost, XGBoost, GBDT, LightGBM, CatBoost, TabNet, ANN, Super TML, and Wide&Deep). As a result, TabNet was chosen to construct the prediction model with the best performance (accuracy = 0.82 and AUC = 0.83). Afterward, four variables that strongly correlated with lapatinib dose were ranked
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