50 citations · 125 across the 8 of their papers we have counts for
6 papers · 1 filter
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types
Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan +9
With the growing interest in large language models, the need for evaluating the quality of machine text compared to reference (typically human-generated) text has become focal atte…
Do We Still Need Clinical Language Models?
Eric Lehman, Evan Hernandez, Diwakar Mahajan +7
Although recent advances in scaling large language models (LLMs) have resulted in improvements on many NLP tasks, it remains unclear whether these models trained primarily with gen…
Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models
Giridhar Kaushik Ramachandran, Kevin Lybarger, Yaya Liu +5
An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate pati…
SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)
Nancy X. R. Wang, Diwakar Mahajan, Marina Danilevsky +1
Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this p…
Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives
Diwakar Mahajan, Jennifer J Liang, Ching-Huei Tsou
Understanding medication events in clinical narratives is essential to achieving a complete picture of a patient's medication history. While prior research has explored classificat…
Extracting Daily Dosage from Medication Instructions in EHRs: An Automated Approach and Lessons Learned
Diwakar Mahajan, Jennifer J. Liang, Ching-Huei Tsou
Medication timelines have been shown to be effective in helping physicians visualize complex patient medication information. A key feature in many such designs is a longitudinal re…