335 citations · 589 across the 12 of their papers we have counts for
6 papers · 1 filter
Improving the Fairness of Chest X-ray Classifiers
Haoran Zhang, Natalie Dullerud, Karsten Roth +3
Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays. However, prior work has…
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021
Fabian Falck, Yuyin Zhou, Emma Rocheteau +7
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021. This index is not complete, as some accepted abstracts chose to opt-out of inclusi…
Federated and Differentially Private Learning for Electronic Health Records
Stephen R. Pfohl, Andrew M. Dai, Katherine Heller
The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predi…
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…
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Anand Avati, Stephen Pfohl, Chris Lin +7
The paper develops machine‑learning models using eight years of Stanford Hospital electronic health records to predict which inpatients will be discharged within the next 24 hours,…
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
Stephen Pfohl, Ben Marafino, Adrien Coulet +3
Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cho…