732 citations · 1.3k across the 23 of their papers we have counts for
6 papers · 1 filter
ML4H Abstract Track 2020
Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck +3
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020. This index is not complete, as some accepted abstracts chose to opt-out…
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
Matthew B. A. McDermott, Bret Nestor, Evan Kim +4
Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks. It has been used extensively on various d…
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
Matthew B. A. McDermott, Tzu Ming Harry Hsu, Wei-Hung Weng +2
It is often infeasible or impossible to obtain ground truth labels for medical data. To circumvent this, one may build rule-based or other expert-knowledge driven labelers to inges…
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
Haoran Zhang, Amy X. Lu, Mohamed Abdalla +2
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance…
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott +2
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we ex…
ML4H Abstract Track 2019
Matthew B. A. McDermott, Emily Alsentzer, Sam Finlayson +5
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019. This index is not complete, as some accepted abstracts chose to opt-out…