960 citations · 3.4k across the 60 of their papers we have counts for
12 papers · 1 filter
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
The Limits of Fair Medical Imaging AI In The Wild
Yuzhe Yang, Haoran Zhang, Judy W Gichoya +2
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Prior…
Event-Based Contrastive Learning for Medical Time Series
Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott +4
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse o…
Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals
Hyewon Jeong, Collin M. Stultz, Marzyeh Ghassemi
Heart failure is a debilitating condition that affects millions of people worldwide and has a significant impact on their quality of life and mortality rates. An objective assessme…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception
Jiyoung Lee, Seungho Kim, Seunghyun Won +6
AI alignment refers to models acting towards human-intended goals, preferences, or ethical principles. Given that most large-scale deep learning models act as black boxes and canno…
Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit
Ming Ying Yang, Gloria Hyunjung Kwak, Tom Pollard +2
Social determinants of health (SDOH) -- the conditions in which people live, grow, and age -- play a crucial role in a person's health and well-being. There is a large, compelling…
In the Name of Fairness: Assessing the Bias in Clinical Record De-identification
Yuxin Xiao, Shulammite Lim, Tom Joseph Pollard +1
Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic heal…
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT
Edward H. Lee, Brendan Kelly, Emre Altinmakas +27
While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoreg…
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi +3
Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of MI is intractable, even if the true joint probability de…
Change is Hard: A Closer Look at Subpopulation Shift
Yuzhe Yang, Haoran Zhang, Dina Katabi +1
Machine learning models often perform poorly on subgroups that are underrepresented in the training data. Yet, little is understood on the variation in mechanisms that cause subpop…
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning
Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi
In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These s…