نتایج جستجو برای: gail risk model
تعداد نتایج: 2934993 فیلتر نتایج به سال:
BACKGROUND We have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population. METHODS A polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression a...
Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has potential to with high-dimensional observations as input, e.g., images. By applying a real robot, perhaps robot be obtained for daily activities like washing, folding clothes, cooking, and cleaning. However, human demonstration data are often imperfec...
Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through training in single-agent environments, but they have difficulty in generalizing to multi-agent driving scenarios. We argue these difficulties arise ...
BACKGROUND As the risks and benefits of early detection and primary prevention strategies for breast cancer are beginning to be quantified, the risk perception of women has become increasingly important as may affect their screening behaviors. This study evaluated the women's breast cancer risk perception and their accuracy, and determined the factors that can affect their risk perception accur...
OBJECTIVE To review the statistical approaches to test for spatial heterogeneity of relative risks. Three different statistical tests (Gail, Martuzzi-Hills and Potthoff-Whittinghill) are reviewed and applied--as motivating example--to the analysis of cause-specific mortality records (years: 1991-2000) of the Municipalities belonging to the Local Health Unit Alto Vicentino. METHODS Spatial het...
I would like to thank the editors for the invitation to comment on “Projecting Individualized Probabilities of Developing Breast Cancer for White Females Who Are Being Examined Annually” (1) and to recognize coauthors of that article, Louise A. Brinton, David P. Byar, Donald K. Corle, Sylvan B. Green, Catherine Schairer, and John J. Mulvihill, who contributed key insights and analyses. Several ...
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