ITMO University, Saint Petersburg, Russia.
National Almazov Medical Research Centre, Saint Petersburg, Russia.
Stud Health Technol Inform. 2021 Nov 18;287:55-56. doi: 10.3233/SHTI210811.
The important information about a patient is often stored in a free-form text to describe the events in the patient's medical history. In this work, we propose and evaluate a hybrid approach based on rules and syntactical analysis to normalise temporal expressions and assess uncertainty depending on the remoteness of the event. A dataset of 500 sentences was manually labelled to measure the accuracy. On this dataset, the accuracy of extracting temporal expressions is 95,5%, and the accuracy of normalization is 94%. The event extraction accuracy is 74.80%. The essential advantage of this work is the implementation of the considered approach for the non-English language where NLP tools are limited.
患者的重要信息通常以自由格式的文本存储，用于描述患者病史中的事件。在这项工作中，我们提出并评估了一种基于规则和句法分析的混合方法，以规范化时间表达式并根据事件的遥远程度评估不确定性。我们使用一个包含 500 个句子的数据集进行手动标注，以衡量准确性。在这个数据集上，提取时间表达式的准确性为 95.5%，规范化的准确性为 94%。事件提取的准确性为 74.80%。这项工作的主要优势在于针对英语以外的语言实施了所考虑的方法，在这些语言中，NLP 工具受到限制。
