Papers by Yujuan Fu
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods (2024.lrec-main)
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Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J. Dobbins, Namu Park, Michael Leu, Abby R. Rosenberg, Kevin Lybarger, Fei Xia, Özlem Uzuner, Meliha Yetisgen
| Challenge: | Social determinants of health (SDoH) are often studied in the electronic health record (EHR) however, there are difficulties in documenting SDoH in a tabular format due to the lack of a comprehensive SDoh tool. |
| Approach: | They propose to annotate social history sections from 1,260 clinical notes from pediatric patients within the University of Washington (UW) hospital system. |
| Outcome: | The proposed corpus captures ten distinct health determinants including living and economic stability, prior trauma, education access, substance use history, and mental health with an overall annotator agreement of 81.9 F1. |
To Err Is Human, How about Medical Large Language Models? Comparing Pre-trained Language Models for Medical Assessment Errors and Reliability (2024.lrec-main)
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| Challenge: | a 1999 report found that at least forty thousand deaths are a result of preventable medical errors. |
| Approach: | They test pre-trained language models to characterize their error generation and reliability in medical assessment ability. |
| Outcome: | The results show that pre-trained models can generate errors and perform better than human models. |
Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions (2025.findings-naacl)
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| Challenge: | Large language models have demonstrated great performance across various benchmarks, but data contamination is a concern in their evaluation. |
| Approach: | They analyze 50 papers on data contamination detection and test three of them as case studies to identify the possibility of data contamination. |
| Outcome: | The proposed methods can detect membership inference attacks on instance-level data, and can perform similar to random guessing on LLM pretraining datasets. |
MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes (2025.findings-acl)
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| Challenge: | Several studies have shown that large language models can answer medical questions correctly, outperforming the average human score in some medical exams. |
| Approach: | They introduce MEDEC, the first publicly available benchmark for medical error detection and correction in clinical notes. |
| Outcome: | The proposed model outperforms medical doctors in errors detection and correction tasks. |