4 papers
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…
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…
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…
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…