activity
20192022
most citedGoogle COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)

16 citations · 22 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2022

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

Gerardo Flores, George H. Chen, Tom Pollard +2

A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022. This index is incomplete as some authors of invited non-archival pres…

cs.CR202016 cited

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

Shailesh Bavadekar, Andrew Dai, John Davis +27

This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19sympt…

cs.LG20196 cited

Modelling EHR timeseries by restricting feature interaction

Kun Zhang, Yuan Xue, Gerardo Flores +3

Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests. The patterns of these values may be s…

cs.LG2019

Explaining an increase in predicted risk for clinical alerts

Michaela Hardt, Alvin Rajkomar, Gerardo Flores +5

Much work aims to explain a model's prediction on a static input. We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estima…

cs.LG2019

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

Edward Choi, Zhen Xu, Yujia Li +4

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure…