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BRP-GABLE：适用于多种结果类型的可解释规则预测框架AI 解读

Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationships. Automatic Binary Logistic Estimation (ABLE) provides interpretable rule-based representations by constructing additive models using threshold rules. However, the original method is restricted to binary outcomes, and its greedy forward construction follows a search path, making candidate rules…

医疗数据中的联邦规则集成方法AI 解读

Machine learning has become integral to medical research and is increasingly applied in clinical settings to support diagnosis and decision-making; however, its effectiveness depends on access to large, diverse datasets, which are limited within single institutions. Although integrating data across institutions can address this limitation, privacy regulations and data ownership constraints hinder these efforts. Federated learning enables collaborative model training without…

结合 Group LASSO 扩展 Bagging MARS 的异质性处理效应估计AI 解读

Recent years, large scale clinical data like patient surveys and medical record data are playing an increasing role in medical data science. These large-scale clinical data, collectively referred to as "real-world data (RWD)". It is expected to be widely used in large-scale observational studies of specific diseases, personal medicine or precise medicine, finding the responder of drugs or treatments. Applying RWD for estimating heterogeneous treat ment effect (HTE) has…

考虑主效应的生存因果规则集成方法：异质性处理效应估计AI 解读

With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with different characteristics are always heterogeneous, and various heterogeneous treatment effect machine learning estimation methods have been proposed owing to their flexibility and high prediction accuracy. However, most machine…

兼顾主效应的异质性处理效应因果规则集成方法AI 解读

This study proposes a novel framework based on the RuleFit method to estimate Heterogeneous Treatment Effect (HTE) in a randomized clinical trial. To achieve this, we adopted S-learner of the metaalgorithm for our proposed framework. The proposed method incorporates a rule term for the main effect and treatment effect, which allows HTE to be interpretable form of rule. By including a main effect term in the proposed model, the selected rule is represented as an HTE that…

用于异质性处理效应估计的组 Lasso 规则集成方法AI 解读

The increasing scientific attention given to precision medicine based on real-world data has led many recent studies to clarify the relationships between treatment effects and patient characteristics. However, this is challenging because of ubiquitous heterogeneity in the treatment effect for individuals and the real-world data on their background being complex and noisy. Because of their flexibility, various heterogeneous treatment effect (HTE) machine learning (ML)…