Enhancing Oncology-Specific Question Answering With Large Language Models Through Fine-Tuned Embeddings With Synthetic Data
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an enhanced retrieval-augmented generation (RAG) model for oncology electronic health records (EHRs). The new model significantly improves the accuracy and relevance of retrieving clinical notes for cancer-related queries.
Area Of Science
- Medical Informatics
- Natural Language Processing
- Oncology Data Extraction
Background
- Advancements in retrieval-augmented generation (RAG) and large language models (LLMs) have transformed real-world evidence extraction from electronic health records (EHRs).
- Extracting precise clinical information from unstructured oncology EHRs remains a challenge, impacting research and patient care.
Purpose Of The Study
- To enhance RAG effectiveness for oncology EHRs by developing a specialized retriever encoder.
- To improve the precision and relevance of retrieved clinical notes for oncology-specific queries.
Main Methods
- Pretraining a retriever encoder on over six million oncology notes from 209,135 patients.
- Fine-tuning the model as a sentence transformer using 12,371 LLM-synthesized query-passage pairs.
- Evaluating retrieval performance against six embedding models using NDCG, Precision, and Recall metrics on 50 oncology questions.
Main Results
- The developed model outperformed the runner-up by 9% in NDCG, 7% in Precision, and 6% in Recall (top 10 results).
- Exceptional retrieval performance was observed across all metrics for key oncology categories such as diagnosis, disease status, and tumor characteristics.
- The model demonstrated superior ability in retrieving pertinent clinical notes from oncology EHRs.
Conclusions
- Pretrained contextual embeddings and sentence transformers are effective for retrieving relevant oncology EHR notes.
- LLM-synthesized query-passage pairs offer a viable data augmentation strategy for specialized domains.
- This fine-tuning approach shows promise for improving data extraction in healthcare settings with limited annotated data.
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