960 citations · 3.4k across the 60 of their papers we have counts for
15 papers · 1 filter
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian +2
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patie…
Confounding Feature Acquisition for Causal Effect Estimation
Shirly Wang, Seung Eun Yi, Shalmali Joshi +1
Reliable treatment effect estimation from observational data depends on the availability of all confounding information. While much work has targeted treatment effect estimation fr…
Improving Dialogue Breakdown Detection with Semi-Supervised Learning
Nathan Ng, Marzyeh Ghassemi, Narendran Thangarajan +2
Building user trust in dialogue agents requires smooth and consistent dialogue exchanges. However, agents can easily lose conversational context and generate irrelevant utterances.…
Multiple Sclerosis Severity Classification From Clinical Text
Alister D Costa, Stefan Denkovski, Michal Malyska +5
Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and r…
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings
Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg +1
Machine learning models in health care are often deployed in settings where it is important to protect patient privacy. In such settings, methods for differentially private (DP) le…
Ethical Machine Learning in Health Care
Irene Y. Chen, Emma Pierson, Sherri Rose +3
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical consideratio…
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi
Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve…
Probabilistic Machine Learning for Healthcare
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi +1
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review,…
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
Karsten Roth, Timo Milbich, Björn Ommer +2
Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown…
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
Joseph Paul Cohen, Lan Dao, Paul Morrison +8
Purpose: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the pr…
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…
COVID-19 Image Data Collection: Prospective Predictions Are the Future
Joseph Paul Cohen, Paul Morrison, Lan Dao +3
Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main ima…
Counterfactually Guided Off-policy Transfer in Clinical Settings
Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi
Domain shift, encountered when using a trained model for a new patient population, creates significant challenges for sequential decision making in healthcare since the target doma…
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…