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Found 4 papers, 1 papers with code
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record
Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant.
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection
When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading.
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.
