Weighted random forest for estimating individualized treatment rules
10.3760/cma.j.cn112338-20250108-00020
- VernacularTitle:基于加权随机森林的个体治疗规则估计
- Author:
Ziyu ZHAO
1
;
Mengyi LU
;
Fang SHAO
;
Dongfang YOU
;
Yang ZHAO
Author Information
1. 南京医科大学公共卫生学院生物统计学系，南京 211166
- Publication Type:Journal Article
- Keywords:
Personalized medicine;
Individualized treatment rules;
Causal inference;
Random forest
- From:
Chinese Journal of Epidemiology
2025;46(8):1431-1437
- CountryChina
- Language:Chinese
-
Abstract:
With the rapid development of personalized medicine, recommending the optimal treatment regimes among multiple options for individual patients has become a key topic in the study of individualized treatment rules. Existing methods often face challenges such as limited accuracy and robustness when handling multi-category treatment problems. This study proposes a weighted random forest method that formulates the treatment decision problem as a weighted classification task. By incorporating the expected loss differences among treatment outcomes, the method enhances its learning process and improves recommendation performance with the non-parametric nature and flexibility of random forests. The weighted random forest method is further applied to real-world hypertension intervention data to generate personalized antihypertensive treatment recommendations based on the patient's baseline characteristics, demonstrating its potential value in clinical practice. This research aims to provide a new approach for individualized treatment rules in multi-treatment settings and to support the development of data-driven clinical decision-making systems.
