Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA.
Virginia Commonwealth University, 401 S. Main St., Richmond, VA 23284, USA.
J Biomed Inform. 2021 Jun;118:103784. doi: 10.1016/j.jbi.2021.103784. Epub 2021 Apr 14.
Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and providing good care. Frequently, important information regarding a patient's medical timeline is buried in their Electronic Health Record (EHR) in the form of unstructured clinical notes. This results in care providers spending time reading notes in a patient's record in order to become familiar with their condition prior to developing a diagnosis or treatment plan. Valuable time could be saved if this information was readily accessible for searching and visualization for fast comprehension by the medical team. Clinical Natural Language Processing (NLP) is an area of research that aims to build computational methods to automatically extract medically relevant information from unstructured clinical texts. A key component of Clinical NLP is Temporal Reasoning, as understanding a patient's medical history relies heavily on the ability to identify, assimilate, and reason over temporal information. In this work, we review the current state of Temporal Reasoning in the clinical domain with respect to Clinical Timeline Extraction. While much progress has been made, the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Areas such as handling relative and implicit temporal expressions, both in normalization and in identifying temporal relationships, improving co-reference resolution, and building inter-operable timeline extraction tools that can integrate multiple types of data are in need of new and innovative solutions to improve performance on clinical data.
了解患者的病史，例如症状持续的时间或进行某项操作的时间，对于诊断问题和提供良好的护理至关重要。通常，有关患者医疗时间轴的重要信息以非结构化临床笔记的形式隐藏在他们的电子健康记录 (EHR) 中。这导致护理人员花费时间阅读患者记录中的笔记，以便在制定诊断或治疗计划之前了解其病情。如果可以快速方便地搜索和可视化此信息，以供医疗团队快速理解，那么就可以节省宝贵的时间。临床自然语言处理 (NLP) 是一个研究领域，旨在构建计算方法，以便从非结构化临床文本中自动提取与医学相关的信息。临床 NLP 的一个关键组成部分是时间推理，因为理解患者的病史在很大程度上依赖于识别、吸收和推理时间信息的能力。在这项工作中，我们回顾了临床领域中与临床时间线提取相关的时间推理的当前状态。尽管已经取得了很大的进展，但在实际应用于临床环境之前，当前的最先进技术仍有很长的路要走。需要新的创新解决方案来处理相对和隐含的时间表达，包括规范化和识别时间关系、改进共指解析以及构建可集成多种类型数据的可互操作的时间线提取工具，以提高对临床数据的性能。
