CLD-MEC at MEDIQA- CORR 2024 Task: GPT-4 Multi-Stage Clinical Chain of Thought Prompting for Medical Errors Detection and Correction
Renad Alzghoul, Ayaabdelhaq Ayaabdelhaq, Abdulrahman Tabaza, Ahmad Altamimi
Abstract
This paper demonstrates CLD-MEC team submission to the MEDIQA-CORR 2024 shared task for identifying and correcting medical errors from clinical notes. We developed a framework to track two main types of medical errors: diagnostics and medical management-related errors. The tracking framework is implied utilizing a GPT-4 multi-stage prompting-based pipeline that ends with the three downstream tasks: classification of medical error existence (Task 1), identification of error location (Task 2), and correction error (Task 3). Throughout the pipeline, we employed clinical Chain of Thought (CoT) and Chain-of-Verification (CoVe) techniques to mitigate the hallucination and enforce the clinical context learning. The model performance is acceptable, given it is based on zero-shot learning. In addition, we developed a RAG system injected with clinical practice guidelines as an external knowledge datastore. Our RAG is based on the Bio_ClinicalBERT as a vector embedding model. However, our RAG system failed to get the desired results. We proposed recommendations to be investigated in future research work to overcome the limitations of our approach.- Anthology ID:
- 2024.clinicalnlp-1.52
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 537–556
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.52
- DOI:
- 10.18653/v1/2024.clinicalnlp-1.52
- Cite (ACL):
- Renad Alzghoul, Ayaabdelhaq Ayaabdelhaq, Abdulrahman Tabaza, and Ahmad Altamimi. 2024. CLD-MEC at MEDIQA- CORR 2024 Task: GPT-4 Multi-Stage Clinical Chain of Thought Prompting for Medical Errors Detection and Correction. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 537–556, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- CLD-MEC at MEDIQA- CORR 2024 Task: GPT-4 Multi-Stage Clinical Chain of Thought Prompting for Medical Errors Detection and Correction (Alzghoul et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2024.clinicalnlp-1.52.pdf