6 citations · 8 across the 12 of their papers we have counts for
14 papers
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…
Geographically Regularized AUC-Maximizing Personalized Federated Learning
Mayu Hiraishi, Kensuke Tanioka, Toshio Shimokawa
Accurate diagnostic and risk-prediction models are important for supporting clinical decision-making during infectious disease outbreaks. However, privacy and governance requiremen…
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…
Principal component-guided sparse reduced-rank regression
Kanji Goto, Shintaro Yuki, Kensuke Tanioka +1
Reduced-rank regression estimates regression coefficients by imposing a low-rank constraint on the matrix of regression coefficients, thereby accounting for correlations among resp…
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…
Regularized Sparse Optimal Discriminant Clustering
Mayu Hiraishi, Kensuke Tanioka, Hiroshi Yadohisa
We propose a new method based on sparse optimal discriminant clustering (SODC), incorporating a penalty term into the scoring matrix based on convex clustering. With the addition o…
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…
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…
Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset
Kyoka Yoshiok, Kensuke Tanioka, Satoru Hiwa +1
Early detection of esophagitis is important because this condition can progress to cancer if left untreated. However, the accuracies of different deep learning models in detecting…
Wilcoxon-type Multivariate Cluster Elastic Net
Mayu Hiraishi, Kensuke Tanioka, Hiroshi Yadohisa
We propose a method for high dimensional multivariate regression that is robust to random error distributions that are heavy-tailed or contain outliers, while preserving estimation…
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…
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…
Estimation and visualization of treatment effects for multiple outcomes
Shintaro Yuki, Kensuke Tanioka, Hiroshi Yadohisa
We consider a randomized controlled trial between two groups. The objective is to identify a population with characteristics such that the test therapy is more effective than the c…