Year: 2022
Type: review
Abstract: 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.
Source: npj Digital Medicine
Authors: Honghan Wu, Minhong Wang, Jinge Wu, Farah Francis, Yun-Hsuan Chang, Alex Shavick, Hang Dong, Michael T. C. Poon, Natalie Fitzpatrick, Adam P. Levine, Karin T. Slater, Alex Handy, Andreas Karwath, Georgios V. Gkoutos, Claude Chelala, Anoop Dinesh Shah, Robert Stewart, Nigel Collier, Beatrice Alex, William Whiteley, Cathie Sudlow, Angus Roberts, Richard J. B. Dobson
Institutions: University College London, University of Edinburgh, University of Oxford, University of Birmingham, University College London Hospitals NHS Foundation Trust, Queen Mary University of London, Barts Cancer Institute, King's College London, South London and Maudsley NHS Foundation Trust, University of Cambridge
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,290
Funders: South London and Maudsley NHS Foundation Trust, King's College Hospital NHS Foundation Trust, Cancer Research UK, National Institute for Health and Care Research, British Council, King's College London, University of Edinburgh, Medical Research Council, Engineering and Physical Sciences Research Council, National Institute for Health Research Applied Research Collaboration South London, Health Data Research UK
Awards: G1168647334, G190134582, G1933108020, G4537443053, Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC), DATAMIND: Data Hub for Mental health INformatics research Development, Deriving an actionable patient phenome from healthcare data, Deriving an actionable patient phenome from healthcare data, The theory of algebraic cycles on an arithmetical perspective., Semantic-based secure infrastructure for interoperable, in-depth biomedical data and analytics, Violence, Health and Society: VISION, G8208521676, G8774690691
OpenAlex ID: W4312113143