在模拟诊断轨迹时平衡可解释性和灵活性,使用嵌入式神经过程模型
Yuankang Zhao1, Matthew M Engelhard1
1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
概括
本研究介绍了Hawkes过程 (HP) 的新灵活影响内核,用于模拟事件序列. 该方法平衡了灵活性和可解释性,这对于电子健康记录 (EHR) 至关重要.
科学领域:
- 计算统计的计算统计.
- 机器学习在医疗保健中的应用
背景情况:
- 霍克斯过程 (HP) 模型具有自我强化动态的事件序列,在电子健康记录 (EHR) 中很常见.
- 传统的HP提供了可解释性,但缺乏灵活性,而神经网络的HP则灵活,但更难以解释.
- 在医疗保健事件序列分析中,存在对可解释但又灵活的模型的关键需求.
研究的目的:
- 开发一种新的霍克斯过程 (HP) 公式,在不牺牲事件序列建模性能的情况下增强可解释性.
- 解决基于神经网络的HP中灵活性和可解释性之间的权衡问题.
- 为了使大量事件类型的大规模事件序列的建模.
主要方法:
- 提出了一个灵活的冲击内核,在事件嵌入空间中作为神经网络实例化.
- 在事件中建模影响功能,嵌入空间以增强灵活性和可解释性.
- 集成的可选变压器编码器层进一步将事件嵌入置于背景,允许在灵活性和可解释性之间进行可调节的权衡.
主要成果:
- 拟议的方法在模拟研究中准确地恢复了冲击函数.
- 在MIMIC-IV程序数据集上取得了竞争性表现.
- 在杜克-EHR儿童诊断数据集上展示了具有临床意义的解释,即使没有变压器层.
结论:
- 灵活的影响内核有效地捕捉了电子健康记录和其他事件序列数据中的自我强化动态.
- 在先进的HP模型中,可以保持可解释性,而不会影响预测性能.
- 这种方法为关键医疗保健应用中的可解释事件序列建模提供了一个有希望的解决方案.
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