activity
20152021
most citedScalable and accurate deep learning for electronic health records

2.7k citations · 3.1k across the 21 of their papers we have counts for

collaborators
Showing 2019Show all

5 papers · 1 filter

cs.LG2019★ 2 cited

The accuracy vs. coverage trade-off in patient-facing diagnosis models

Anitha Kannan, Jason Alan Fries, Eric Kramer +3

A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis…

cs.LG2019★ 3 cited

Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare

Scott L. Fleming, Kuhan Jeyapragasan, Tony Duan +4

There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learn…

cs.LG2019★ 10 cited

Counterfactual Reasoning for Fair Clinical Risk Prediction

Stephen Pfohl, Tony Duan, Daisy Yi Ding +1

The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for hi…

cs.CY2019

Medical device surveillance with electronic health records

Alison Callahan, Jason A Fries, Christopher Ré +4

Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real…

q-bio.QM2019★ 58 cited

A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data

Albee Y. Ling, Allison W. Kurian, Jennifer L. Caswell-Jin +3

Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary infor…