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使用配备知识图表注意力网络的多任务学习来检测精神和身体障碍.

Wei Zhang1, Ling Kong1, Soobin Lee2

  • 1School of Information Management, Nanjing Agricultural University, Nanjing 210095, China; Department of Library and Information Science, Yonsei University, Seoul 03722, Republic of Korea.

Artificial intelligence in medicine
|March 10, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的联合建模方法,用于同时检测心理和身体障碍 (MPD). 开发的模型和公开可用的数据集为精神病学医学和并发症研究推进了医疗信息学.

关键词:
图表注意力网络 图表注意力网络知识图表知识图表精神障碍 精神障碍是一种精神障碍.在Muti-task学习中学习.身体上的障碍 身体上的障碍

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算语言学 计算语言学
  • 精神病体医学是一种精神病体医学.

背景情况:

  • 精神和身体障碍 (MPD) 相互关联,但当前的研究往往会分别分析它们.
  • 现有的医疗信息学方法缺乏对精神和身体健康方面的同时关注.
  • 这种差距阻碍了对精神病体疾病和并发症的全面理解和检测.

研究的目的:

  • 提出和验证一种联合建模方法,用于同时检测精神和身体疾病 (MPD).
  • 从在线医疗咨询记录中开发一个全面的MPD知识本体和知识图.
  • 构建一个细粒度的MPD体和一个多任务学习模型来检测严重程度.

主要方法:

  • 抓取了在线医疗咨询记录,以构建一个MPD知识本体和知识图 (12673个节点,82,195个关系).
  • 通过专家指导的注释创建了一个MPD语料库 (8909条记录) 与严重程度 (无到危险) 通过专家指导注释.
  • 设计了一种多任务学习模型,结合了知识图注意网络 (KGAT) 来检测MPD严重程度.

主要成果:

  • 证明了拟议的联合建模方法在检测MPD严重性的有效性.
  • 利用基于本体学的和基于中心性的方法来推断额外的知识,提高KGAT的预测性能和可解释性.
  • 开发的MPD数据集已公开供进一步研究使用.

结论:

  • 联合建模方法有效地解决了同时检测精神和身体疾病 (MPD).
  • 创建的MPD知识图和语料库为医学信息学研究提供了宝贵的资源.
  • 这些发现有助于在精神病学医学,精神病学和身体共患病的更好理解和检测.