Papers by Kevin Lybarger
A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models (2024.lrec-main)
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Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer Lewis, Aashka Damani, Özlem Uzuner, Martin Gunn, Meliha Yetisgen
| Challenge: | Medical imaging reports document radiologists' interpretation of medical images through detailed narrative text. |
| Approach: | They propose a corpus of annotated medical imaging reports (CAMIR) that includes 609 annotation radiology reports from three imaging modality types. |
| Outcome: | The proposed schema captures clinical indications, lesions, and medical problems and can be used in secondary applications. |
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. |
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation (2022.naacl-srw)
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| Challenge: | Cognitive distortions are one of the targets of cognitive behavioral therapy (CBT). |
| Approach: | They propose to use Easy Data Augmentation, back translation, and mixup techniques to detect distortions in text-based therapy messages. |
| Outcome: | The proposed methods improve performance with optimized parameter settings for rare classes with an augmented model, MentalBERT. |
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation (2026.findings-eacl)
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| Challenge: | Retrieval-augmented generation (RAG) is a common technique for grounding language models in domain-specific information. |
| Approach: | They propose a new retrieval technique that incorporates diversity into the retrieval step to improve performance on reasoning-intensive QA benchmarks. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive QA benchmarks by 4–10%. |