隐藏的病人连接:预测激素治疗药物停用使用超图神经网络在临床通信
Qingyuan Song1, Yunfei Hu1, Congning Ni1
1Vanderbilt University, Nashville, Tennessee, USA.
概括
预测乳腺癌患者中断激素治疗是非常重要的. 我们的研究表明,使用超图神经网络分析临床通信可以通过发现隐藏的患者联系来提高预测准确性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 激素治疗是乳腺癌的关键辅助治疗方法.
- 对于荷尔蒙疗法的药物停用率是显著的.
- 了解和预测停药对于患者的结果至关重要.
研究的目的:
- 为了建模临床通信,预测激素治疗药物停用.
- 为了研究超图神经网络在这个预测任务中的实用性.
主要方法:
- 利用超图神经网络 (HNN) 来分析来自临床通信的患者连接.
- 综合临床沟通内容,包括患者人口统计,保险和癌症阶段数据.
- 将HNN性能与基线模型进行比较,例如图形卷积网络,随机森林和支持向量机.
主要成果:
- 在HNN模型中,曲线下的面积 (AUC) 为67.9%.
- 这种表现显著超过了基线模型:GCN (65.3%),随机森林 (62.7%) 和SVM (62.8%).
- 从临床通信中整合隐藏的患者连接,提高了预测性能.
结论:
- 在临床沟通中隐藏的患者联系可以显著改善药物停用预测模型.
- 超图神经网络显示出对分析医疗数据中复杂关系的前景.
- 未来的工作应该将结构化医疗记录与临床通信相结合,以提高预测准确度.
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