430 citations · 468 across the 5 of their papers we have counts for
6 papers
Disability prediction in multiple sclerosis using performance outcome measures and demographic data
Subhrajit Roy, Diana Mincu, Lev Proleev +8
Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for dis…
Healthsheet: Development of a Transparency Artifact for Health Datasets
Negar Rostamzadeh, Diana Mincu, Subhrajit Roy +7
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems…
Concept-based model explanations for Electronic Health Records
Diana Mincu, Eric Loreaux, Shaobo Hou +7
Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse outcomes in electronic health records (EHRs) due to their ability to encode past clinical states.…
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan +37
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline i…
Inferring Javascript types using Graph Neural Networks
Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette +1
The recent use of `Big Code' with state-of-the-art deep learning methods offers promising avenues to ease program source code writing and correction. As a first step towards automa…
Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?
Jessica Schrouff, Janaina Mourao-Miranda
Since machine learning models have been applied to neuroimaging data, researchers have drawn conclusions from the derived weight maps. In particular, weight maps of classifiers bet…