1.5k citations · 1.9k across the 18 of their papers we have counts for
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Diagnosing our datasets: How does my language model learn clinical information?
Furong Jia, David Sontag, Monica Agrawal
Large language models (LLMs) have performed well across various clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) dat…
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models
Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse +3
Patients often face difficulties in understanding their hospitalizations, while healthcare workers have limited resources to provide explanations. In this work, we investigate the…
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang +3
We study the application of large language models to zero-shot and few-shot classification of tabular data. We prompt the large language model with a serialization of the tabular d…
Large Language Models are Few-Shot Clinical Information Extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang +2
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the gener…
Co-training Improves Prompt-based Learning for Large Language Models
Hunter Lang, Monica Agrawal, Yoon Kim +1
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data. While prompting has emerged as a promising par…
Robust Benchmarking for Machine Learning of Clinical Entity Extraction
Monica Agrawal, Chloe O'Connell, Yasmin Fatemi +2
Clinical studies often require understanding elements of a patient's narrative that exist only in free text clinical notes. To transform notes into structured data for downstream u…
TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes
Monica Agrawal, Griffin Adams, Nathan Nussbaum +1
Oral drugs are becoming increasingly common in oncology care. In contrast to intravenous chemotherapy, which is administered in the clinic and carefully tracked via structure elect…