From the 1 of 16 linked papers with an AI index.
10 citations · 24 across the 6 of their papers we have counts for
16 papers
Ontology-driven weak supervision for clinical entity classification in electronic health records
Jason A. Fries, Ethan Steinberg, Saelig Khattar +4
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform…
A new paradigm for accelerating clinical data science at Stanford Medicine
Somalee Datta, Jose Posada, Garrick Olson +7
Stanford Medicine is building a new data platform for our academic research community to do better clinical data science. Hospitals have a large amount of patient data and research…
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data
Ethan Steinberg, Ken Jung, Jason A. Fries +3
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process…
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…
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…
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…