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Pingshan Liu

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Proceedings Article | 13 July 2024 Paper

Construction of medical information knowledge graph question and answer system based on deep learning

Donghua Mo, Pingshan Liu

Proceedings Volume 13208, 132081H (2024) https://doi.org/10.1117/12.3036593

KEYWORDS: Data modeling, Matrices, Intelligence systems, Education and training, Diseases and disorders, Databases, Medicine, Deep learning, Performance modeling, Visualization

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In view of the shortcomings of existing medical information intelligent question-and-answer systems in terms of data sources, user needs understanding and answer accuracy, this research aims to build a medical information questionanswering system based on deep learning and knowledge graph. Through the utilization of the BERT-GCN model, as well as knowledge graph, intelligent question-and-answer, and network crawler technologies, we have gathered, extracted, and integrated medical data from the Internet extensively. The system's key features include a knowledge graph, keyword matching, and automatic question-and-answer capabilities, facilitating the retrieval and visualization of medical information knowledge. By efficiently answering questions, users are able to access the necessary medical knowledge and knowledge graph in a user-friendly manner. This system effectively assists patients and healthcare professionals in accessing, comprehending, and utilizing medical information, thereby advancing the progress of the healthcare industry

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