960 citations · 3.4k across the 60 of their papers we have counts for
7 papers · 1 filter
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan +4
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operat…
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz +1
Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, mod…
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…
ClinicalVis: Supporting Clinical Task-Focused Design Evaluation
Marzyeh Ghassemi, Mahima Pushkarna, James Wexler +2
Making decisions about what clinical tasks to prepare for is multi-factored, and especially challenging in intensive care environments where resources must be balanced with patient…
Racial Disparities and Mistrust in End-of-Life Care
Willie Boag, Harini Suresh, Leo Anthony Celi +2
There are established racial disparities in healthcare, including during end-of-life care, when poor communication and trust can lead to suboptimal outcomes for patients and their…
Modeling Mistrust in End-of-Life Care
Willie Boag, Harini Suresh, Leo Anthony Celi +2
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score. We show that this score has statistically significant racial association…
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…