This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.
The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.
The named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease\'s presence and symptoms prevalence in patients.
A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.
目的:本研究旨在利用自然语言处理(NLP)提取关键信息(临床因素,健康的社会决定因素)来自文献中公布的案例。
方法:提出的框架集成了一个数据层,用于从临床病例报告中准备数据队列;一个NLP层,用于在文本中找到临床和人口统计学命名的实体和关系;以及一个用于基准性能和分析的评估层。本研究的重点是从COVID-19病例报告中提取有价值的信息。
结果:与基准方法相比,NLP层中的命名实体识别实现实现了约1-3%的性能增益。此外,即使没有大量的数据标签,关系提取方法在准确性方面优于基准方法(提高1-8%)。彻底的检查显示疾病的存在和患者的症状患病率。
结论:类似的方法可以推广到其他传染病。在研究其他传染病时,使用通过迁移学习获得的先验知识是值得的。