Papers by J Ross Mitchell
Not What the Doctor Ordered: Surveying LLM-based De-identification and Quantifying Clinical Information Loss (2025.emnlp-main)
Copied to clipboard
Kiana Aghakasiri, Noopur Zambare, JoAnn Thai, Carrie Ye, Mayur Mehta, J Ross Mitchell, Mohamed Abdalla
| Challenge: | De-identification is an application of NLP where automated algorithms remove identifying information of patients and providers. |
| Approach: | They propose to use generative large language models to de-identify patients and providers . they propose to validate existing metrics to quantify extent of inappropriate removal . |
| Outcome: | The proposed method is based on a survey of LLM-based de-identification research . it shows that the models perform poorly in identifying clinically relevant changes . |
Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches (2026.findings-eacl)
Copied to clipboard
| Challenge: | a recent study has not examined their generalizability between formats, cultures, and genders. |
| Approach: | They evaluate large language models (LLMs) and small LLMs at clinical de-identification . they show that smaller models achieve comparable performance while substantially reducing inference cost . |
| Outcome: | The proposed models outperform larger models in de-identification tasks with limited data . the models can be fine-tuned with limited datasets to outperformed larger models . |