Abstract

Objective

Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity. It is urgent to develop an early risk prediction method that can adaptively integrate both static and dynamic health data.

Materials and Methods

Data were from 6367 patients with Peptic Ulcer Bleeding between 2007 and 2016. This article develops a novel End-to-end Importance-Aware Personalized Deep Learning Approach (eiPDLA) to achieve accurate early clinical risk prediction. Specifically, eiPDLA introduces a long short-term memory with temporal attention to learn sequential dependencies from time-stamped records and simultaneously incorporating a residual network with correlation attention to capture their influencing relationship with static medical data. Furthermore, a new multi-residual multi-scale network with the importance-aware mechanism is designed to adaptively fuse the learned multisource features, automatically assigning larger weights to important features while weakening the influence of less important features.

Results

Extensive experimental results on a real-world dataset illustrate that our method significantly outperforms the state-of-the-arts for early risk prediction under various settings (eg, achieving an AUC score of 0.944 at 1 year ahead of risk prediction). Case studies indicate that the achieved prediction results are highly interpretable.

Conclusion

These results reflect the importance of combining static and dynamic health data, mining their influencing relationship, and incorporating the importance-aware mechanism to automatically identify important features. The achieved accurate early risk prediction results save precious time for doctors to timely design effective treatments and improve clinical outcomes.

中文翻译：

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使用静态和动态健康数据进行早期风险预测的重要性感知个性化学习

摘要

客观的

准确的风险预测对于评估早期医疗效果和提高医疗质量具有重要意义。现有方法通常是针对动态医学数据设计的，需要长期观察。同时，由于潜在的不确定性和不可量化的模糊性，重要的个性化静态信息被忽略了。迫切需要开发一种能够自适应地整合静态和动态健康数据的早期风险预测方法。

材料和方法

数据来自 2007 年至 2016 年间的 6367 名消化性溃疡出血患者。本文开发了一种新颖的端到端重要性感知个性化深度学习方法 (eiPDLA)，以实现准确的早期临床风险预测。具体来说，eiPDLA 引入了具有时间注意的长短期记忆，以从时间戳记录中学习顺序依赖关系，同时结合具有相关注意的残差网络来捕获它们与静态医学数据的影响关系。此外，设计了一种新的具有重要性感知机制的多残差多尺度网络，以自适应地融合学习到的多源特征，自动为重要特征分配更大的权重，同时削弱次要特征的影响。

结果

在真实世界数据集上的大量实验结果表明，我们的方法在各种设置下的早期风险预测方面显着优于现有技术（例如，在风险预测前 1 年达到 0.944 的 AUC 分数）。案例研究表明，实现的预测结果具有高度可解释性。

结论

这些结果反映了结合静态和动态健康数据，挖掘它们的影响关系，并结合重要性感知机制来自动识别重要特征的重要性。所获得的准确的早期风险预测结果为医生及时设计有效的治疗方案和改善临床结果节省了宝贵的时间。