Scott J. Hebbring

According to our database, Scott J. Hebbring authored at least 9 papers between 2015 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2022

The <i>All of Us</i> Research Program: Data quality, utility, and diversity.

Andrea H. Ramirez, Lina M. Sulieman, David J. Schlueter, Alese E. Halvorson, Jun Qian, Francis Ratsimbazafy, Roxana Loperena-Cortes, Kelsey R. Mayo, Melissa A. Basford, Nicole Deflaux, Karthik Muthuraman, Karthik Natarajan, Abel N. Kho, Hua Xu, Consuelo H. Wilkins, Hoda Anton-Culver, Eric Boerwinkle, Mine Cicek, Cheryl R. Clark, Elizabeth Cohn, Lucila Ohno-Machado, Sheri D. Schully, Brian K. Ahmedani, Maria Argos, Robert M. Cronin, Christopher J. O'Donnell, Mona Fouad, David B. Goldstein, Philip Greenland, Scott J. Hebbring, Elizabeth W. Karlson, Parinda Khatri, Bruce Korf, Jordan W. Smoller, Stephen Sodeke, John Wilbanks, Justin Hentges, Stephen Mockrin, Christopher Lunt, Stephanie A. Devaney, Kelly Gebo, Joshua C. Denny, Robert J. Carroll, David Glazer, Paul A. Harris, George Hripcsak, Anthony A. Philippakis, Dan M. Roden

Patterns, 2022

2021

E-Pedigrees: a large-scale automatic family pedigree prediction application.

Xiayuan Huang, Nicholas P. Tatonetti, Katie Larow, Brooke Delgoffee, John Mayer, David Page, Scott J. Hebbring

Bioinform., 2021

Medical Big Data Analysis System to Discover Associations between Genetic Variants and Diseases.

Daehee Kim, Stephanie Gamboa, Vanessa Hernandez, Marlen Martinez-Lopez, Scott J. Hebbring, John Mayer, Jamie Fox

Proceedings of the ICC 2021, 2021

2020

High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records.

Alexander K. Taylor, Ross Kleiman, Scott J. Hebbring, Peggy L. Peissig, David Page

CoRR, 2020

2019

High-Throughput Machine Learning from Electronic Health Records.

Ross S. Kleiman, Paul S. Bennett, Peggy L. Peissig, Richard L. Berg, Zhaobin Kuang, Scott J. Hebbring, Michael D. Caldwell, David Page

CoRR, 2019

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43, 870 individuals from the eMERGE network.

Xinyuan Zhang, Yogasudha Veturi, Shefali Setia Verma, William Bone, Anurag Verma, Anastasia Lucas, Scott J. Hebbring, Joshua C. Denny, Ian B. Stanaway, Gail P. Jarvik, David R. Crosslin, Eric B. Larson, Laura Rasmussen-Torvik, Sarah A. Pendergrass, Jordan W. Smoller, Hakon Hakonarson, Patrick Sleiman, Chunhua Weng, David Fasel, Wei-Qi Wei, Iftikhar J. Kullo, Daniel J. Schaid, Wendy K. Chung, Marylyn D. Ritchie

Proceedings of the Biocomputing 2019: Proceedings of the Pacific Symposium, 2019

2018

Applying family analyses to electronic health records to facilitate genetic research.

Xiayuan Huang, Robert C. Elston, Guilherme J. M. Rosa, John Mayer, Zhan Ye, Terrie E. Kitchner, Murray H. Brilliant, David Page, Scott J. Hebbring

Bioinform., 2018

Use of Electronic Health Record to Predict Family Relationships for Phenome-wide Research.

Xiayuan Huang, Robert C. Elston, John Mayer, Zhan Ye, Yuqi He, David Page, Scott J. Hebbring

Proceedings of the AMIA 2018, 2018

2015

Application of clinical text data for phenome-wide association studies (PheWASs).

Scott J. Hebbring, Majid Rastegar-Mojarad, Zhan Ye, John Mayer, Crystal Jacobson, Simon M. Lin

Bioinform., 2015