A Deep Dive into GPT-4's Data Mining Capabilities for Free-Text Spine Radiology Reports

Klaudia Szabó Ledenyi, András Kicsi, László Vidács, László Vidács

2024

Abstract

The significant growth of large language models revolutionized the field of natural language processing. Recent advancements in large language models, particularly generative pretrained transformer (GPT) models, have shown advanced capabilities in natural language understanding and reasoning. These models typically interact with users through prompts rather than providing training data or fine-tuning, which can save a significant amount of time and resources. This paper presents a study evaluating GPT-4’s performance in data mining from free-text spine radiology reports using a single prompt. The evaluation includes sentence classification, sentence-level sentiment analysis and two representative biomedical information extraction tasks: named entity recognition and relation extraction. Our research findings indicate that GPT-4 performs effectively in few-shot information extraction from radiology text, even without specific training for the clinical domain. This approach shows potential for more effective information extraction from free-text radiology reports compared to manual annotation.

Paper Citation

in Harvard Style

Szabó Ledenyi K., Kicsi A. and Vidács L. (2024). A Deep Dive into GPT-4's Data Mining Capabilities for Free-Text Spine Radiology Reports. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 82-92. DOI: 10.5220/0012765100003756

in Bibtex Style

@conference{data24,
author={Klaudia Szabó Ledenyi and András Kicsi and László Vidács},
title={A Deep Dive into GPT-4's Data Mining Capabilities for Free-Text Spine Radiology Reports},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={82-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012765100003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - A Deep Dive into GPT-4's Data Mining Capabilities for Free-Text Spine Radiology Reports
SN - 978-989-758-707-8
AU - Szabó Ledenyi K.
AU - Kicsi A.
AU - Vidács L.
PY - 2024
SP - 82
EP - 92
DO - 10.5220/0012765100003756
PB - SciTePress