Gerardo Flores

According to our database, Gerardo Flores authored at least 9 papers between 2018 and 2022.

Collaborative distances:

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

2022

A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022.

Gerardo Flores, George H. Chen, Tom J. Pollard, Joyce C. Ho, Tristan Naumann

CoRR, 2022

Conference on Health, Inference, and Learning (CHIL) 2022.

Gerardo Flores, George H. Chen, Tom J. Pollard, Ayah Zirikly, Michael C. Hughes, Tasmie Sarker, Joyce C. Ho, Tristan Naumann

Proceedings of the Conference on Health, Inference, and Learning, 2022

2020

Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0).

Shailesh Bavadekar, Andrew M. Dai, John Davis, Damien Desfontaines, Ilya Eckstein, Katie Everett, Alex Fabrikant, Gerardo Flores, Evgeniy Gabrilovich, Krishna Gadepalli, Shane Glass, Rayman Huang, Chaitanya Kamath, Dennis Kraft, Akim Kumok, Hinali Marfatia, Yael Mayer, Benjamin Miller, Adam Pearce, Irippuge Milinda Perera, Venky Ramachandran, Karthik Raman, Thomas Roessler, Izhak Shafran, Tomer Shekel, Charlotte Stanton, Jacob Stimes, Mimi Sun, Gregory Wellenius, Masrour Zoghi

CoRR, 2020

Explaining an increase in predicted risk for clinical alerts.

Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew M. Dai, Michael D. Howell, Greg Corrado, Claire Cui, Moritz Hardt

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer.

Edward Choi, Zhen Xu, Yujia Li, Michael Dusenberry, Gerardo Flores, Emily Xue, Andrew M. Dai

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Modelling EHR timeseries by restricting feature interaction.

Kun Zhang, Yuan Xue, Gerardo Flores, Alvin Rajkomar, Claire Cui, Andrew M. Dai

CoRR, 2019

Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records.

Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai

CoRR, 2019

2018

Scalable and accurate deep learning with electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Gregory S. Corrado, Jeffrey Dean

npj Digit. Medicine, 2018

Scalable and accurate deep learning for electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc V. Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg Corrado, Jeff Dean

CoRR, 2018