2.2k citations · 2.2k across the 4 of their papers we have counts for
4 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…
Interpretable machine learning: definitions, methods, and applications
W. James Murdoch, Chandan Singh, Karl Kumbier +2
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for pre…
Concise comparative summaries (CCS) of large text corpora with a human experiment
Jinzhu Jia, Luke Miratrix, Bin Yu +4
In this paper we propose a general framework for topic-specific summarization of large text corpora and illustrate how it can be used for the analysis of news databases. Our framew…