42 citations · 47 across the 5 of their papers we have counts for
5 papers
Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making
Aliyah R. Hsu, Yeshwanth Cherapanamjeri, Briton Park +3
Pre-trained transformers are often fine-tuned to aid clinical decision-making using limited clinical notes. Model interpretability is crucial, especially in high-stakes domains lik…
Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data
Nick Altieri, Briton Park, Mara Olson +3
Precision medicine has the potential to revolutionize healthcare, but much of the data for patients is locked away in unstructured free-text, limiting research and delivery of effe…
Stable discovery of interpretable subgroups via calibration in causal studies
Raaz Dwivedi, Yan Shuo Tan, Briton Park +4
Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC…
Curating a COVID-19 data repository and forecasting county-level death counts in the United States
Nick Altieri, Rebecca L. Barter, James Duncan +11
As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curatio…
Modeling the spread of the Zika virus using topological data analysis
Derek Lo, Briton Park
Zika virus (ZIKV), a disease spread primarily through the Aedes aegypti mosquito, was identified in Brazil in 2015 and was declared a global health emergency by the World Health Or…