From the 1 of 6 linked papers with an AI index.
45 citations · 55 across the 2 of their papers we have counts for
6 papers
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…
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…
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
Daisy Yi Ding, Chloé Simpson, Stephen Pfohl +3
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical infor…