Innovative Applications of Natural Language Processing in Medical Diagnosis Texts in the Era of Large Models

DOI: https://doi.org/10.62517/jike.202404413

Author(s)

Fanhua Wang

Affiliation(s)

School of Computer Science and Technology, Huaqiao University Xiamen, China

Abstract

This study explores the innovative applications of Natural Language Processing (NLP) technologies in medical diagnosis texts within the context of large models. With the rapid advancement of deep learning, large-scale models such as GPT-3 and WuDao 2.0, characterized by their massive parameter sizes, have achieved remarkable progress in NLP. This work outlines the critical roles of large models in processing medical diagnosis texts, including text generation, machine translation, summarization, and question-answering systems. Employing state-of-the-art deep learning algorithms and extensive medical diagnosis datasets, we trained and optimized large models to enhance their semantic understanding and reasoning capabilities. Comparative analyses between traditional NLP approaches and large models demonstrate the latter's superiority in improving diagnostic accuracy and efficiency. The findings highlight that large models not only significantly advance the performance of medical text processing but also offer new perspectives and solutions for the medical domain, especially in interpreting complex medical texts and supporting clinical decision-making.

Keywords

Natural Language Processing; Large Models; Medical Diagnosis; Deep Learning; Text Analysis

References

[1] He Jianhu, Wang Dejian, Zhao Zhirui, et al. Language model in cutting-edge research and innovation in the field of medical application [J]. Journal of medical informatics, 2024, 45 (9): 10 to 18. [2] Zheng Han Yang Ming. Application and innovation of topic modeling technology in publishing research: from text mining to theory construction [J]. China Digital Publishing, 2024(6). [3] Hou Lei. Research on Chinese medical entity recognition based on hybrid deep learning model [D]. Henan University of Technology,2022. [4] Liu Xinzhu. Application of relation extraction technology for Medical Texts [D]. University of Chinese Academy of Sciences,2022. [5] Chen Jian, Zuo Xiuran, Sun Miao. Research and application of BERT in unstructured medical text data processing [J]. China Digital Medicine, 2022(002):017. [6] Li Jianfeng. Applying external knowledge of Chinese named entity recognition research and its application in medical areas [D]. Harbin industrial university, 2016. [7] Wei Sirui, Chen Liuqing. Big language model in the application of dermatology outlook [J]. Journal of digital medical and health, 2024 (3): 179-184. [8] Wang Y. Research on text mining technology and its application in teaching platform [D]. Beijing Jiaotong University [2025-02-17].