Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Tanioka, Kensuke"'
Recently, from the personalized medicine perspective, there has been an increased demand to identify subgroups of subjects for whom treatment is effective. Consequently, the estimation of heterogeneous treatment effects (HTE) has been attracting atte
Externí odkaz:
http://arxiv.org/abs/2407.19659
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 demonstrated in a population that meets the eligibility criteria, identify
Externí odkaz:
http://arxiv.org/abs/2407.17534
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 differ
Externí odkaz:
http://arxiv.org/abs/2309.11914
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 met
Externí odkaz:
http://arxiv.org/abs/2307.14766
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 esophagitis have yet to be compared. Thus, this study aimed to compare
Externí odkaz:
http://arxiv.org/abs/2301.02390
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 accuracy in normal random error distributions. We extend the Wilcoxon
Externí odkaz:
http://arxiv.org/abs/2209.13354
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 ubiquitou
Externí odkaz:
http://arxiv.org/abs/2206.08576
Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a type of deep learning, can removes such artifacts. Various architectu
Externí odkaz:
http://arxiv.org/abs/2201.00084
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 control therapy. Such a population is called a subgroup. This identific
Externí odkaz:
http://arxiv.org/abs/2108.00163
Before new clinical measurement methods are implemented in clinical practice, it must be confirmed whether their results are equivalent to those of existing methods. The agreement of the trend between these methods is evaluated using the four-quadran
Externí odkaz:
http://arxiv.org/abs/2007.04042