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Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones +15
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held…
A Review of Challenges and Opportunities in Machine Learning for Health
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam +3
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. Howe…
Why Is My Classifier Discriminatory?
Irene Chen, Fredrik D. Johansson, David Sontag
Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this…