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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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基于图形的临床推器:使用图形表示学习预测专家程序订单.

Sajjad Fouladvand1, Federico Reyes Gomez2, Hamed Nilforoshan2

  • 1Biomedical Informatics Research, Stanford University, Stanford, CA, United States of America.

Journal of biomedical informatics
|June 4, 2023
PubMed
概括
此摘要是机器生成的。

图形神经网络改善了内分泌学和血液学专业护理预测. 这些先进的模型优于传统的检查清单,提高了患者获得及时医疗专业知识的机会.

关键词:
电子医疗咨询 电子医疗咨询内分泌学 在内分泌学.图形神经网络是一个神经网络.血液学 血液学 血液学

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 获得专业医疗护理的机会有限,导致诊断和治疗的重大延迟.
  • 自动推系统可以简化患者转诊和专家评估.

研究的目的:

  • 评估图形神经网络 (GNN) 模型在预测内分泌学和血液学咨询需求方面的有效性.
  • 将基于GNN的预测与标准护理检查清单和现有的医疗建议算法进行比较.

主要方法:

  • 开发了一种使用结构化电子健康记录的新型异质图神经网络模型.
  • 制定了预测后续的专业订单作为一个链接预测问题在图表框架内.
  • 在内分泌学和血液学专科护理站点的数据上训练和评估模型.

主要成果:

  • 与之前的系统相比,GNN模型显示了内分泌学ROC-AUC的8%改善 (0.88) 和血液学的5%改善 (0.84).
  • 对于内分泌学转诊,GNN推器比临床检查清单获得了更高的精度 (0.60) 和F1得分 (0.37).
  • 此外,GNN模型在精度 (0.44) 和F1得分 (0.41) 方面也超过了血液学转诊的检查清单.

结论:

  • 图形神经网络模型可以显著提高数字专业咨询系统的准确性.
  • 将GNN整合到临床工作流程中可以通过利用类似过去病例的模式来改善获得专业医疗专业知识的机会.