1 citations · 1 across the 5 of their papers we have counts for
5 papers
Estimating heterogeneous treatment effects by W-MCM based on Robust reduced rank regression
Ryoma Hieda, Shintaro Yuki, Kensuke Tanioka +1
Recently, from the personalized medicine perspective, there has been an increased demand to identify subgroups of subjects for whom treatment is effective. Consequently, the estima…
Extension of W-method and A-learner for multiple binary outcomes
Shintaro Yuki, Kensuke Tanioka, Hiroshi Yadohisa
In this study, we compared two groups, in which subjects were assigned to either the treatment or the control group. In such trials, if the efficacy of the treatment cannot be demo…
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…
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…
Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging
Taira Watanabe, Kensuke Tanioka, Satoru Hiwa +1
Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a…