Early diagnosis of disease can result in improved health outcomes, such as higher survival rates and lower treatment costs. With the massive amount of information 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 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, reporting 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 and pulmonary heart disease, and is also able to produce patient risk trajectories over time that can be exploited for further predictions.

翻译：疾病早期诊断可以改善健康结果,如存活率提高和治疗费用降低。由于电子健康记录中有大量信息,因此极有可能使用机器学习(ML)方法来模拟疾病进展,以早期预测疾病发病和其他结果。在这项工作中,我们利用最近对神经观察器的创新来利用神经观察器的全部时间信息。我们建议ICE-NODE(将临床嵌入的嵌入与神经普通差异等值相结合),这种结构在时间上结合了临床编码和神经观察器的嵌入,以学习和预测电子健康记录中的病人轨迹。我们将我们的方法应用于公开提供的MIMIC-III和MIMIC-IV数据集,报告与最新方法相比更好的预测结果,特别是用于在环境HR中不常见的临床编码。我们还表明,ICE-NODE更有能力预测某些医疗状况,如急性肾衰竭和肺部心脏病等,并且能够对病人的轨迹进行进一步风险预测。