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 Mousa Altamimi · 2024
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 managementrelated errors.The tracking framework is implied utilizing a GPT-4 multi-stage promptingbased 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.