DKADE:基于深度学习和知识图的新框架,用于识别药物不良事件和相关药物
Ze-Ying Feng1,2, Xue-Hong Wu3,4, Jun-Long Ma1
1Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha 410083, China.
Briefings in bioinformatics
|June 21, 2023
概括
DKADE是一个新的框架,通过推断缺少的药物信息和评估相关性来增强药物不良事件 (ADE) 的检测. 这种深度学习方法可以改善药物安全监测,并识别新的药物不良反应.
科学领域:
- 药物监督 药物监督 药物监督
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 药物不良事件 (ADEs) 带来了重大的临床风险和资源负担.
- 现有的自然语言处理 (NLP) 系统与不完整的药物信息和复杂的语义扎,特别是在中文中.
- 对于自动化ADE检测系统的需求非常大,特别是对于中文语言.
研究的目的:
- 开发一个强大的框架,DKADE,用于自动化药物不良事件 (ADE) 检测.
- 解决目前NLP系统中关于缺失药物实体和多语言适用性的局限性.
- 为了使新药不良反应的查.
主要方法:
- 提出了DKADE,这是一个结合深度学习和知识图的框架,用于ADE识别.
- 实施实体推断缺少的药物信息和相关性评估.
- 综合药物订单与现有的药物知识库.
主要成果:
- 在实验评估中,DKADE获得了91.13%的高整体F1得分.
- 该框架在真实世界的临床数据上展示了适应性和有效性.
- 成功推断出缺失的药物实体,并评估了ADE相关性.
结论:
- 德凯德为改善药物安全性研究和自动化不良事件监测提供了强大的解决方案.
- 该框架增强了NLP系统用于ADE检测的适应性,包括中文.
- 德凯德有助于识别潜在的新药不良反应,有助于提高患者的安全性.
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