背景:在慢性病领域需要补充和超越循证医学的新方法,鉴于这种疾病在全球人口中的发病率越来越高。一个有希望的途径是二次使用电子健康记录(EHR),分析患者数据以进行临床和转化研究。基于机器学习处理EHR的方法提高了对患者临床轨迹和慢性病风险预测的理解,创造一个独特的机会来获得以前未知的临床见解。然而,在自由形式的文本中,大量的临床病史仍然被锁定在临床叙述之后。因此,释放EHR数据的全部潜力取决于自然语言处理(NLP)方法的发展,以自动将临床文本转换为结构化临床数据,可以指导临床决策并可能延迟或预防疾病发作。
目的:研究的目的是全面概述应用于与慢性病相关的自由文本临床笔记的NLP方法的发展和吸收,包括调查NLP方法在理解临床叙事方面面临的挑战。
方法:遵循系统审查和荟萃分析(PRISMA)指南的首选报告项目,并使用“临床笔记”在5个数据库中进行搜索,\"\"自然语言处理,\"和\"慢性疾病\"及其变化作为关键词,以最大限度地覆盖文章。
结果:在所考虑的2652篇文章中,106符合纳入标准。对纳入的论文进行审查,确定了43种慢性病,然后使用国际疾病分类将其进一步分为10种疾病类别,第十次修订。大多数研究集中在循环系统疾病(n=38),而内分泌和代谢疾病最少(n=14)。这是由于与代谢疾病相关的临床记录的结构,通常包含更多的结构化数据,与循环系统疾病的医疗记录相比,它们更多地关注非结构化数据,因此看到了NLP的更多关注。审查表明,与基于规则的方法相比,机器学习方法的使用显着增加;但是,深度学习方法仍然是新兴的(n=3)。因此,大多数作品都集中在疾病表型的分类上,只有少数论文涉及从自由文本中提取合并症或将临床笔记与结构化数据整合。有一个值得注意的使用相对简单的方法,例如浅层分类器(或与基于规则的方法的组合),由于预测的可解释性,对于更复杂的方法来说,这仍然是一个重要的问题。最后,公开可用数据的稀缺也可能导致更先进方法的开发不足,例如从临床笔记中提取词嵌入。
结论:仍然需要努力改善(1)临床NLP方法从提取到理解的进展;(2)识别实体之间的关系,而不是孤立的实体;(3)了解过去的时间提取,电流,和未来的临床事件;(4)利用临床知识的替代来源;(5)大规模,去识别的临床身体。
BACKGROUND: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset.
OBJECTIVE: The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives.
METHODS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using \"clinical notes,\" \"natural language processing,\" and \"chronic disease\" and their variations as keywords to maximize coverage of the articles.
RESULTS: Of the 2652 articles considered, 106 met the inclusion criteria.
Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The
review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes.
CONCLUSIONS: Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.