activity
20222026
collaborators

7 papers

stat.ME2026

BRP-GABLE: An Interpretable Rule-Based Prediction Framework for Multiple Outcome Types

Yulong Li, Ke Wan, Toshio Shimokawa +1

Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationship…

cs.LG2026

Federated Rule Ensemble Method in Medical Data

Ke Wan, Kensuke Tanioka, Toshio Shimokawa

Machine learning has become integral to medical research and is increasingly applied in clinical settings to support diagnosis and decision-making; however, its effectiveness depen…

stat.ML2025

Causal rule ensemble approach for multi-arm data

Ke Wan, Kensuke Tanioka, Toshio Shimokawa

Heterogeneous treatment effect (HTE) estimation is critical in medical research. It provides insights into how treatment effects vary among individuals, which can provide statistic…

stat.ME2024

Extention of Bagging MARS with Group LASSO for Heterogeneous Treatment Effect Estimation

Guanwenqing He, Ke Wan, Kazushi Maruo +1

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, collec…

stat.ME2023

Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects

Ke Wan, Kensuke Tanioka, Toshio Shimokawa

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 p…

stat.ME2023

Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of main effects

Mayu Hiraishi, Ke Wan, Kensuke Tanioka +2

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-lea…

stat.ME2022

Rules Ensemble Method with Group Lasso for Heterogeneous Treatment Effect Estimation

Ke Wan, Kensuke Tanioka, Toshio Shimokawa

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 pati…