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Hao H Zhang

Showing results (1-10 of 13) with videos related to

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Statistics in Medicine|February 3, 2007
Variable selection for proportional odds modelWenbin Lu, Hao H Zhang
Physics in Medicine and Biology|March 13, 2010
The minimum knowledge base for predicting organ-at-risk dose-volume levels and plan-related complications in IMRT planningHao H Zhang, Robert R Meyer, Leyuan Shi, et al.
International Journal of Radiation Oncology, Biology, Physics|July 21, 2009
Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT frameworkHao H Zhang, Warren D D'Souza, Leyuan Shi, et al.
Oral Oncology|December 29, 2009
Moderate predictive value of demographic and behavioral characteristics for a diagnosis of HPV16-positive and HPV16-negative head and neck cancerGypsyamber D'Souza, Hao H Zhang, Warren D D'Souza, et al.
Physics in Medicine and Biology|June 5, 2008
A nested partitions framework for beam angle optimization in intensity-modulated radiation therapyWarren D D'Souza, Hao H Zhang, Daryl P Nazareth, et al.
Physics in Medicine and Biology|January 15, 2010
A two-stage sequential linear programming approach to IMRT dose optimizationHao H Zhang, Robert R Meyer, Jianzhou Wu, et al.
International Journal of Radiation Oncology, Biology, Physics|May 22, 2007
A multiplan treatment-planning framework: a paradigm shift for intensity-modulated radiotherapyRobert R Meyer, Hao H Zhang, Laura Goadrich, et al.
AIDS Care|June 4, 2024
Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data studyRuilie Cai, Xueying Yang, Yunqing Ma, et al.
Journal of Acquired Immune Deficiency Syndromes (1999)|November 19, 2024
Using Machine Learning Techniques to Predict Viral Suppression Among People With HIVXueying Yang, Ruilie Cai, Yunqing Ma, et al.
Plos One|July 18, 2020
Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic studyWei-Hsuan Lo-Ciganic, James L Huang, Hao H Zhang, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Statistics in Medicine|February 3, 2007
Variable selection for proportional odds modelWenbin Lu, Hao H Zhang
Physics in Medicine and Biology|March 13, 2010
The minimum knowledge base for predicting organ-at-risk dose-volume levels and plan-related complications in IMRT planningHao H Zhang, Robert R Meyer, Leyuan Shi, et al.
International Journal of Radiation Oncology, Biology, Physics|July 21, 2009
Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT frameworkHao H Zhang, Warren D D'Souza, Leyuan Shi, et al.
Oral Oncology|December 29, 2009
Moderate predictive value of demographic and behavioral characteristics for a diagnosis of HPV16-positive and HPV16-negative head and neck cancerGypsyamber D'Souza, Hao H Zhang, Warren D D'Souza, et al.
Physics in Medicine and Biology|June 5, 2008
A nested partitions framework for beam angle optimization in intensity-modulated radiation therapyWarren D D'Souza, Hao H Zhang, Daryl P Nazareth, et al.
Physics in Medicine and Biology|January 15, 2010
A two-stage sequential linear programming approach to IMRT dose optimizationHao H Zhang, Robert R Meyer, Jianzhou Wu, et al.
International Journal of Radiation Oncology, Biology, Physics|May 22, 2007
A multiplan treatment-planning framework: a paradigm shift for intensity-modulated radiotherapyRobert R Meyer, Hao H Zhang, Laura Goadrich, et al.
AIDS Care|June 4, 2024
Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data studyRuilie Cai, Xueying Yang, Yunqing Ma, et al.
Journal of Acquired Immune Deficiency Syndromes (1999)|November 19, 2024
Using Machine Learning Techniques to Predict Viral Suppression Among People With HIVXueying Yang, Ruilie Cai, Yunqing Ma, et al.
Plos One|July 18, 2020
Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic studyWei-Hsuan Lo-Ciganic, James L Huang, Hao H Zhang, et al.
Pageof 2