نتایج جستجو برای: gail risk model
تعداد نتایج: 2934993 فیلتر نتایج به سال:
Imitation learning aims at recovering expert policies from limited demonstration data. Generative Adversarial Learning (GAIL) employs the generative adversarial framework for imitation and has shown great potentials. GAIL its variants, however, are found highly sensitive to hyperparameters hard converge well in practice. One key issue is that supervised discriminator a much faster speed than re...
INTRODUCTION Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women ...
BACKGROUND Adding genotypes from seven single-nucleotide polymorphisms (SNPs), which had previously been associated with breast cancer, to the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) increases the area under the receiver operating characteristic curve from 0.607 to 0.632. METHODS Criteria that are based on four clinical or public health applications were used to...
An open problem in autonomous vehicle safety validation is building reliable models of human driving behavior simulation. This work presents an approach to learn neural policies from real world demonstration data. We model as a sequential decision making that characterized by non-linearity and stochasticity, unknown underlying cost functions. Imitation learning for generating intelligent when t...
OBJECTIVE To characterise the current usage of chemoprevention agents among hospitalised women who are at higher risk for breast cancer. STUDY DESIGN A cross-sectional study. SETTING Academic hospital at Baltimore. PARTICIPANTS A bedside survey of 250 women aged 50-75 years was conducted who were cancer-free at the time of study enrolment and hospitalised to a general medicine service. Re...
Medicare Advantage: Issues, Insights, and Implications for the Future Paul Cotton, Joseph P. Newhouse, PhD, Kevin G. Volpp, MD, PhD, A. Mark Fendrick, MD, Susan Lynne Oesterle, Pat Oungpasuk, Ruchi Aggarwal, Gail Wilensky, PhD, and Kathleen Sebelius Editorial S-2 D.B. Nash, and A.Y. Schwartz The History, Impact, and Future of the Medicare Advantage Star Ratings System S-3 P. Cotton Medica...
نمودار تعداد نتایج جستجو در هر سال
با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید