The automatic extraction of a patient’s natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient’s medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information. In this paper, we introduce a new Natural Language Processing (NLP) framework, capable of extracting the aforementioned elements from EHRs written in Spanish using rule-based methods. We focus on building medical timelines, which include disease diagnosis and its progression over time. By using a large dataset of EHRs comprising information about patients suffering from lung cancer, we show that our framework has an adequate level of performance by correctly building the timeline for 843 patients from a pool of 989 patients, achieving a precision of 0.852.

从电子健康记录 (EHR) 中自动提取患者的自然病史是构建能够推理临床变量并支持决策的智能系统的关键一步。尽管 EHR 包含大量有关患者医疗护理的有价值的信息，但只有在时间背景下进行分析时才能完全理解这些信息。任何智能系统都应该能够从 EHR 的自由文本中提取医学概念、日期表达、时间关系和医学事件的时间顺序；然而，由于 EHR 的领域特定性质、写作质量和这些文本缺乏结构，以及更普遍的冗余信息的存在，这项任务很难解决。在本文中，我们介绍了一种新的自然语言处理 (NLP) 框架，能够使用基于规则的方法从用西班牙语编写的 EHR 中提取上述元素。我们专注于建立医疗时间表，其中包括疾病诊断及其随时间的进展。通过使用包含肺癌患者信息的大型 EHR 数据集，我们通过正确构建 989 名患者池中的 843 名患者的时间线，实现了 0.852 的精度，表明我们的框架具有足够的性能水平。