ArticleTranslational and clinical pharmacology2022
Building and analyzing machine learning-based warfarin dose prediction models using scikit-learn.
Article in Translational and clinical pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.European journal of clinical pharmacology · 2026Review
- Integrating patient-reported weight gain cause narratives into personalized obesity management: a data-driven approach with natural language processing and machine learning.Frontiers in nutrition · 2026Article
- Predicting the progressive-free survival of ALK-positive lung cancer: machine learning algorithm based on habitat imaging.Journal of thoracic disease · 2025Article
- Multiple Strategies Confirm the Anti Hepatocellular Carcinoma Effect of Cinnamic Acid Based on the PI3k-AKT Pathway.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Increasing the Impact and Value of Laboratory Medicine Through Effective and AI-Assisted Communication.EJIFCC · 2025Review
- Validating Machine Learning Models Against the Saline Test Gold Standard for Primary Aldosteronism Diagnosis.JACC. Asia · 2024Article
- Data science through natural language with ChatGPT's Code Interpreter.Translational and clinical pharmacology · 2024Article
- Article
- Intratumoral and peritumoral MRI-based radiomics prediction of histopathological grade in soft tissue sarcomas: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation-based methods.Health science reports · 2023Article
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1 author.
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Abstract
For personalized drug dosing, prediction models may be utilized to overcome the inter-individual variability. Multiple linear regression has been used as a conventional method to model the relationship between patient features and optimal drug dose. However, linear regression cannot capture non-linear relationships and may be adversely affected by non-normal distribution and collinearity of data. To overcome this hurdle, machine learning models have been extensively adapted in drug dose prediction. In this tutorial, random forest and neural network models will be trained in tandem with a multiple linear regression model on the International Warfarin Pharmacogenetics Consortium dataset using the scikit-learn python library. Subsequent model analyses including performance comparison, permutation feature importance computation and partial dependence plotting will be demonstrated. The basic methods of model training and analysis discussed in this article may be implemented in drug dose-related studies.
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