TEE4EHR:转换器事件编码器,用于更好地学习电子健康记录中的代表性
Hojjat Karami1, David Atienza2, Anisoara Ionescu1
1Laboratory of Movement Analysis and Measurements (LMAM), EPFL, Lausanne, Switzerland.
Artificial intelligence in medicine
|June 22, 2024
概括
这项研究介绍了TEE4EHR,这是一个用于电子健康记录 (EHR) 的新型变压器事件编码器 (TEE) 模型. TEE4EHR有效地处理不规则采样的临床数据,改善机器学习模型性能,用于未来事件预测和临床结果预测.
科学领域:
- 机器学习 机器学习
- 生物医学信息学 生物医学信息学
- 时间序列分析时间序列分析
背景情况:
- 电子健康记录 (EHR) 对机器学习提出了挑战,原因是数据采样不规则和非随机缺失值.
- 开发准确的预测模型需要解决这些与临床数据固有的数据复杂性.
研究的目的:
- 引入TEE4EHR,一个变压器事件编码器 (TEE) 模型,包含分析电子健康记录中的事件序列的点过程损失.
- 评估TEE4EHR在自主监督学习和下游临床预测任务中的有效性,使用基准和现实世界EHR数据集.
主要方法:
- 开发了TEE4EHR,它是一种具有点过程损失的变压器事件编码器 (TEE),用于编码实验室测试模式在EHR中.
- 采用自主监督学习方法,与基于注意力的深度神经网络共同训练TEE.
- 提出了一个聚合注意力权重的算法,以了解事件相互作用.
- 转移并结已学习的TEE用于下游结果预测任务.
主要成果:
- 在自主监督学习框架内,TEE在负日志概率和未来事件预测方面表现优异.
- TEE4EHR在处理不规则采样的时间序列以预测临床结果方面超过了最先进的模型.
- 该模型有效地改善了EHR中的表示学习,显示了对临床预测的有用性.
结论:
- TEE4EHR提供了一个强大的机器学习解决方案,用于不规则采样的EHR数据.
- 该模型提高了临床结果的预测准确性,并提高了事件相互作用的可解释性.
- 这种方法推进了深度学习在EHR分析和临床决策支持中的应用.
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