61 citations · 69 across the 4 of their papers we have counts for
5 papers
Towards trustworthy seizure onset detection using workflow notes
Khaled Saab, Siyi Tang, Mohamed Taha +3
A major barrier to deploying healthcare AI models is their trustworthiness. One form of trustworthiness is a model's robustness across different subgroups: while existing models ma…
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Siyi Tang, Jared A. Dunnmon, Liangqiong Qu +4
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in mul…
The Importance of Background Information for Out of Distribution Generalization
Jupinder Parmar, Khaled Saab, Brian Pogatchnik +2
Domain generalization in medical image classification is an important problem for trustworthy machine learning to be deployed in healthcare. We find that existing approaches for do…
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Siyi Tang, Jared A. Dunnmon, Khaled Saab +5
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain un…
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Jared Dunnmon, Alexander Ratner, Nishith Khandwala +8
Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applyi…