图表基于神经网络的子图分析,用于预测药物不良事件
Fangyu Zhou1, Matloob Khushi2, Jonathan Brett3
1School of Project Management, Faculty of Engineering, The University of Sydney, Australia.
Computers in biology and medicine
|October 23, 2024
概括
本研究引入了一种新的基于网络的方法,使用图形神经网络 (GNN) 早期检测药物不良事件 (ADE). 该方法准确地预测了如果,何时以及哪些可能发生的ADE,从而提高了患者的安全性和医疗保健效率.
科学领域:
- 计算医学是一种计算医学.
- 药物监督 药物监督 药物监督
- 机器学习在医疗保健中的应用
背景情况:
- 药物不良事件 (ADEs) 对全球健康构成重大风险,导致住院治疗和死亡.
- 目前的方法往往在广泛使用药物后,迟迟发现ADEs,这对患者的安全构成了挑战.
- 需要计算工具来更早地预测ADEs,在临床试验之前或在临床试验期间.
研究的目的:
- 开发和评估基于网络的计算方法,用于早期识别ADEs.
- 根据患者的诊断史来预测ADEs的发生,时间和类型.
- 为了利用图形神经网络 (GNN) 来增强ADE预测模型.
主要方法:
- 一种基于网络的方法,将患者建模为国际疾病分类 (ICD) 代码的子图.
- 使用了四种图形神经网络 (GNN) 变体 (例如,GraphSage,GAT) 进行预测建模.
- 采用二进制分类来预测发生/时间和多标签分类来预测ADE类型.
主要成果:
- 基于网络的方法在预测ADEs方面表现优异.
- 在预测ADE发生 (0.8863) 和类型 (0.9367) 方面,GraphSage获得了最高的准确性.
- 图表注意网络 (GAT) 在预测ADE时间方面表现最好 (0.8769),对某些ADE类别具有特定优势.
结论:
- 图形神经网络 (GNN) 显示了早期ADE检测和预防的巨大潜力.
- 准确的ADE预测可以为临床决策,预防措施和药物调整提供信息.
- 这种预测方法可以优化医疗保健资源分配,通过防止住院治疗和减少ADEs的负担.
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