论文标题
冰节：将临床嵌入与神经普通微分方程的整合
ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations
论文作者
论文摘要
疾病的早期诊断可以改善健康结果，包括更高的存活率和较低的治疗成本。随着电子健康记录（EHR）中可用的大量信息，使用机器学习（ML）方法具有很大的潜力来对疾病进展进行建模，以旨在早期预测疾病发作和其他结果。在这项工作中，我们采用了最新的神经ODES创新，结合了临床代码丰富的语义嵌入，以利用EHR的全部时间信息。我们提出了冰节（将临床嵌入与神经普通微分方程的整合），该体系结构在时间上整合临床代码和神经ODE的嵌入，以学习和预测EHR中的患者轨迹。我们将我们的方法应用于公共可用的模拟III和模拟IV数据集，并且与最新方法相比，我们发现了改进的预测结果，特别是对于EHR中不经常观察到的临床代码。我们还表明，冰结更有能力预测某些医疗状况，例如急性肾功能衰竭，肺心脏病和与出生有关的问题，在这些问题上，完整的时间信息可以提供重要信息。此外，随着时间的流逝，冰节也能够产生患者的风险轨迹，以进一步详细地预测疾病进化的详细预测。
Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs. With the massive amount of information available in electronic health records (EHRs), there is great potential to use machine learning (ML) methods to model disease progression aimed at early prediction of disease onset and other outcomes. In this work, we employ recent innovations in neural ODEs combined with rich semantic embeddings of clinical codes to harness the full temporal information of EHRs. We propose ICE-NODE (Integration of Clinical Embeddings with Neural Ordinary Differential Equations), an architecture that temporally integrates embeddings of clinical codes and neural ODEs to learn and predict patient trajectories in EHRs. We apply our method to the publicly available MIMIC-III and MIMIC-IV datasets, and we find improved prediction results compared to state-of-the-art methods, specifically for clinical codes that are not frequently observed in EHRs. We also show that ICE-NODE is more competent at predicting certain medical conditions, like acute renal failure, pulmonary heart disease and birth-related problems, where the full temporal information could provide important information. Furthermore, ICE-NODE is also able to produce patient risk trajectories over time that can be exploited for further detailed predictions of disease evolution.
