General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports

Ali H. Dhanaliwala; Rikhiya Ghosh; Sanjeev Kumar Karn; Poikavila Ullaskrishnan; Oladimeji Farri; Dorin Comaniciu; Charles E. Kahn

arXiv · 2311.17213

General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports

Ali H. Dhanaliwala·Rikhiya Ghosh·Sanjeev Kumar Karn·Poikavila Ullaskrishnan·Oladimeji Farri·Dorin Comaniciu·Charles E. Kahn

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Abstract

Radiologists produce unstructured data that can be valuable for clinical care when consumed by information systems. However, variability in style limits usage. Study compares system using domain-adapted language model (RadLing) and general-purpose LLM (GPT-4) in extracting relevant features from chest radiology reports and standardizing them to common data elements (CDEs). Three radiologists annotated a retrospective dataset of 1399 chest XR reports (900 training, 499 test) and mapped to 44 pre-selected relevant CDEs. GPT-4 system was prompted with report, feature set, value set, and dynamic few-shots to extract values and map to CDEs. Output key:value pairs were compared to reference standard at both stages and an identical match was considered TP. F1 score for extraction was 97% for RadLing-based system and 78% for GPT-4 system. F1 score for mapping was 98% for RadLing and 94% for GPT-4; difference was statistically significant (P<.001). RadLing's domain-adapted embeddings were better in feature extraction and its light-weight mapper had better f1 score in CDE assignment. RadLing system also demonstrated higher capabilities in differentiating between absent (99% vs 64%) and unspecified (99% vs 89%). RadLing system's domain-adapted embeddings helped improve performance of GPT-4 system to 92% by giving more relevant few-shot prompts. RadLing system offers operational advantages including local deployment and reduced runtime costs.

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Ali H. Dhanaliwala, Rikhiya Ghosh, Sanjeev Kumar Karn, Poikavila Ullaskrishnan, Oladimeji Farri, Dorin Comaniciu, Charles E. Kahn. 2023-11-28. General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports. https://arxiv.org/abs/2311.17213

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