Papers by Islam Nassar
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)
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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li
| Challenge: | Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes. |
| Approach: | They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy. |
| Outcome: | The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline. |
Generate, Annotate, and Learn: NLP with Synthetic Text (2022.tacl-1)
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| Challenge: | Existing methods to generate unlabeled text are difficult to find. |
| Approach: | They propose a general framework called "generate, annotate, and learn" to take advantage of synthetic text within knowledge distillation, self-training, and few-shot learning applications. |
| Outcome: | The proposed framework achieves state-of-the-art knowledge distillation results for 6-layer transformers on the GLUE leaderboard. |
MedDCR: Learning to Design Agentic Workflows for Medical Coding (2026.findings-acl)
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| Challenge: | Medical coding is the process of translating unstructured clinical notes into standardized diagnostic and procedural codes. |
| Approach: | They propose a closed-loop framework that treats workflow design as a learning problem. |
| Outcome: | The proposed framework outperforms state-of-the-art workflows on benchmark datasets and produces interpretable, adaptable workflows that better reflect real coding practice. |