Yamashita Yasuho

TL;DR: A biomarker based on PD-1 positivity of effector and regulatory T cells in the tumor microenvironment that accurately predicts the effectiveness of checkpoint blockade in patients is developed.

TL;DR: An explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample, and it was found that the point- Wise linear model used genes relevant to the cell cycle-related pathways.

TL;DR: In this paper, a data analysis apparatus (300) executes: a selection process selecting a first variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable groups from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first variable groups in such a manner that the loss function is larger, and operating a second regularization coefficients related to second weight parameter groups corresponding to second feature variables group in such an operation.

TL;DR: In this paper, the authors propose a time series data analysis apparatus that generates first internal data on the basis of first feature quantity data groups, a first internal parameter, and a first learning parameter.

TL;DR: This is the first study on a deep-learning approach to explaining decisions behind readmission predictions and it provides an automatic patient stratification to explain cohorts of readmitted patients.