Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) for learning meaningful representations of medical concepts using graph neural networks over knowledge graphs based on the unified medical language system. These representations are aggregated to represent entire patient visits and then fed into a sequence model to perform predictions at the granularity of multiple hospital visits of a patient. We improve performance by incorporating prior medical knowledge and considering multiple modalities. We compare our method to existing architectures proposed to learn representations at different granularities on the MIMIC-III dataset and show that our approach outperforms these methods. The results demonstrate the significance of multi-modal medical concept representations based on prior medical knowledge.

翻译：临床医生越来越多地寻求借助机器学习来深入理解患者的病程演变。我们提出了一种名为多模态UMLS图学习（MMUGL）的新方法，该方法利用基于统一医学语言系统的知识图谱上的图神经网络，学习医学概念的有意义表示。这些表示被聚合以代表整个患者就诊记录，随后输入到一个序列模型中，从而在患者多次就诊的粒度上进行预测。我们通过融入先验医学知识并考虑多种模态来提升性能。我们将所提方法与现有针对MIMIC-III数据集在不同粒度上学习表示的架构进行了比较，结果表明我们的方法优于这些方法。实验结果证明了基于先验医学知识的多模态医学概念表示的重要性。