用于医学推荐的增强混合图形转换器

Q2 Computer Science
Anil V Turukmane, Sagar Pande, Vaidehi Bedekar, Aditya Kadam
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引用次数: 0

摘要

医疗信息的爆炸式增长产生了大量异构文本医学知识(TMK),这对医疗信息系统至关重要。现有的整合和使用文本医学知识的努力主要集中在建立简单的链接,而不太关注创建准确和快速理解信息的计算机。近年来,自我诊断症状检查器和临床决策支持系统的需求显著增加。现有系统依赖于知识库,这些知识库要么是使用简单的成对统计数据自动生成的,要么是通过耗时的过程手动构建的。该研究探索了学习文本数据的过程,将基于网络的文档中的疾病和症状联系起来。医学概念被废弃,并从不同的网络资源中收集。该研究旨在借助网络文档生成疾病-症状-诊断知识图谱(DSDKG)。将知识图输入到具有注意机制(GAT)的图神经网络中,学习节点和边的关系。最后,将生成式预训练变压器2 (GPT2)封装在强化学习环境中,用于训练模型生成基于文本的推荐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforced Hybrid Graph Transformer for Medical Recommendations
An enormous amount of heterogeneous Textual Medical Knowledge (TMK), which is crucial to healthcare information systems, has been produced by the explosion of healthcare information. Existing efforts to incorporate and use textual medical knowledge primarily concentrate on setting up simple links and pay less attention to creating computers comprehend information accurately and rapidly. Self-diagnostic symptom checkers and clinical decision support systems have seen a significant rise in demand in recent years. Existing systems rely on knowledge bases that are either automatically generated using straightforward paired statistics or manually constructed through a time-consuming procedure. The study explored process to learn textual data, linking disease and symptoms from web-based documents. Medical concepts were scrapped and collected from different web-based sources. The research aims to generate a disease- symptom-diagnosis knowledge graph (DSDKG), with the help of web-based documents. Moreover, the knowledge graph is fed in to Graph neural network with Attention Mechanism (GAT) for learning the nodes and edges relationships. . Lastly Generative Pretrained Transformer 2 (GPT2) all enclosed in a Reinforced learning environment, is used on the trained model to generate text based recommendations.
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来源期刊
EAI Endorsed Transactions on Pervasive Health and Technology
EAI Endorsed Transactions on Pervasive Health and Technology Computer Science-Computer Science (miscellaneous)
CiteScore
3.50
自引率
0.00%
发文量
14
审稿时长
10 weeks
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