381 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of TorontoCA46 papers
- SickKids FoundationCA15 papers
- University College LondonGB11 papers
- Public Health OntarioCA8 papers
- Institute for Clinical Evaluative SciencesCA7 papers
- Stanford UniversityUS5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Toronto Western HospitalCA4 papers
- University of California, BerkeleyUS4 papers
- Centro de Investigación y Docencia EconómicasMX3 papers
- Harvard UniversityUS3 papers
- Johns Hopkins UniversityUS3 papers
6 papers · 1 filter
Synthetic Data Generation for Augmenting Small Samples
Dan Liu, Samer El Kababji, Nicholas Mitsakakis +6
Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
Lin Lawrence Guo, Jason Fries, Ethan Steinberg +6
Foundation models hold promise for transforming AI in healthcare by providing modular components that are easily adaptable to downstream healthcare tasks, making AI development mor…
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Sana Tonekaboni, Danny Eytan, Anna Goldenberg
Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning ge…
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
Erik Drysdale, Devin Singh, Anna Goldenberg
Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting. Existing tools usually make forecasts at the daily or weekly level to ass…
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden +1
Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model to justify its outc…