基于表示学习的病历相似性研究
A Study into patient similarity through representation learning from medical records
摘要：
患者相似性评估，识别与给定患者相似的患者，可以帮助改善医疗护理。可以使用电子病历（EMR）进行评估。患者相似性度量需要将异构EMR转换为可比格式以计算它们的距离。虽然近年来已经开发了多功能的文档表示学习方法，但仍然不清楚应该如何处理复杂的EMR数据以创建最有用的患者表示。这项研究提出了一种新的数据表示方法，电子病历，考虑到临床叙述中的信息。为了解决以前的方法在处理EMR数据的复杂部分的局限性，提出了一种用于构建患者表示的无监督方法，该方法将非结构化数据与从患者的EMR中提取的结构化数据相结合。为了对提取的数据进行建模，我们采用了一种树结构，该结构从EMR中捕获多个医疗事件的时间关系。我们使用不同的工具（如medspaCy，MetaMap和scispaCy）处理临床笔记以提取症状，体征和疾病，并将实体映射到统一医学语言系统（UMLS）。在创建树数据结构后，我们利用两种新的重新标记方法的非叶节点的树捕获两个时间方面的提取的事件。通过遍历树，我们生成了一个序列，可以为每个患者创建嵌入向量。对所提出的用于患者相似性和死亡率预测任务的方法的综合评价表明，我们提出的模型相对于基线具有较低的均方误差（MSE）、较高的精度和归一化的贴现累积增益（NDCG）。
英文摘要：
Patient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods have been developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that takes the information in clinical narratives into account. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs. In order to model the extracted data, we employed a tree structure that captures the temporal relations of multiple medical events from EMR. We processed clinical notes to extract symptoms, signs, and diseases using different tools such as medspaCy, MetaMap, and scispaCy and mapped entities to the Unified Medical Language System (UMLS). After creating a tree data structure, we utilized two novel relabeling methods for the non-leaf nodes of the tree to capture two temporal aspects of the extracted events. By traversing the tree, we generated a sequence that could create an embedding vector for each patient. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
