A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.
A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.
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DOI：
10.1038/s41746-022-00730-6
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发表时间：
2022-12-21
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
--
作者：
实现高质量临床研究所需的大部分知识和信息都以自由文本格式存储。几十年来，自然语言处理（NLP）一直被用来从这些来源中大规模地提取信息。本文旨在对英国过去15年的临床NLP进行全面回顾，以确定社区，描述其演变，分析方法和应用，并确定主要障碍。我们收集了由英国资助者或欧盟资助计划资助的临床NLP项目（n = 94; £ = 41.97 m）的数据集。此外，我们提取了9个资助者，137个组织，139人和431篇研究论文的详细信息。网络是从链接所有实体的时间戳数据创建的，随后应用网络分析来生成见解。作为文献综述的一部分，确定了431篇出版物，其中107篇有资格进行最终分析。结果显示，毫不奇怪，英国的临床NLP在过去15年中大幅增加：2019-2022年期间的总预算是2007-2010年的80倍。然而，需要努力深化疾病（亚）表型等领域并拓宽应用领域。还需要改善学术界和工业界之间的联系，并在现实环境中实现部署，以实现临床NLP在医疗服务中的巨大潜力。主要障碍包括研究和开发对医院数据的访问，在正确的地方缺乏有能力的计算资源，标记数据的稀缺以及共享预训练模型的障碍。
Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.
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