A survey on clinical natural language processing in the United Kingdom from 2007 to 2022
Open accessAbstract
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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Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training.
“Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training.”
From A critical assessment of using ChatGPT for extracting structured data from clinical notes (2024) · cites this paper as [15]
A significant obstacle to developing traditional medical NLP algorithms is the limited existence of human-annotated datasets and the costs associated with new human annotation.
“However, a significant obstacle to developing these traditional medical NLP algorithms is the limited existence of human-annotated datasets and the costs associated with new human annotation15.”
From A critical assessment of using ChatGPT for extracting structured data from clinical notes (2024) · cites this paper as [15]
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Self-citationGorinski, P. J., Wu, H., Grover, C., Tobin, R., Talbot, C., Whalley, H. C., Sudlow, C. L. M., Whiteley, W. N., & Alex, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03985 · Cited by 42 · see citing works
Self-citationDavidson, E. M., Poon, M. T. C., Casey, A., Grivas, A., Duma, D., Dong, H., Suárez-Paniagua, V., Grover, C., Tobin, R., Whalley, H. C., et al. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00671-8 · Cited by 40 · see citing works
Self-citationIrving, J., Colling, C., Shetty, H., Pritchard, M., Stewart, R. J., Fusar‐Poli, P., McGuire, P. K., & Patel, R. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-042949 · Cited by 38 · see citing works
Self-citationWang, X., Rak, R., Restificar, A. C., Nobata, C., Rupp, C., Batista-Navarro, R., Nawaz, R., & Ananiadou, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s8-s11 · Cited by 37 · see citing works
Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages
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Self-citationWang, T., Oliver, D., Msosa, Y. J., Colling, C., Spada, G., Roguski, Ł., Folarin, A., Stewart, R. J., Roberts, A., Dobson, R., et al. (2020) · Journal of Visualized Experiments · doi:10.3791/60794 · Cited by 36 · see citing works
Self-citationNeural networks for open and closed Literature-based Discovery
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Using uncertainty to link and rank evidence from biomedical literature for model curation
Zerva, C., Batista-Navarro, R., Day, P. J., & Ananiadou, S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx466 · Cited by 35 · see citing works
Wesley, E. W., Patel, I., Kadra-Scalzo, G., Pritchard, M., Shetty, H., Broadbent, M. T. M., Segev, A., Patel, R., Downs, J. M., MacCabe, J. H., et al. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.05.006 · Cited by 35 · see citing works
Using text mining techniques to extract phenotypic information from the PhenoCHF corpus
Alnazzawi, N., Thompson, P., Batista-Navarro, R., & Ananiadou, S. (2015) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-15-s2-s3 · Cited by 34 · see citing works
Kugathasan, P., Wu, H., Gaughran, F., Nielsen, R. E., Pritchard, M., Dobson, R., Stewart, R. J., & Stubbs, B. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.10.061 · Cited by 34 · see citing works
Self-citationAnnotation and detection of drug effects in text for pharmacovigilance
Thompson, P., Daikou, S., Ueno, K., Batista-Navarro, R., Tsujii, J., & Ananiadou, S. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0290-y · Cited by 32 · see citing works
The Camden & Islington Research Database: Using electronic mental health records for research
Werbeloff, N., Osborn, D., Patel, R., Taylor, M. J., Stewart, R. J., Broadbent, M. T. M., & Hayes, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190703 · Cited by 31 · see citing works
Self-citationPrediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing
Lineback, C. M., Garg, R., Oh, E. H., Naidech, A. M., Holl, J. L., & Prabhakaran, S. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.649521 · Cited by 31 · see citing works
Korhonen, A., Silins, I., Sun, L., & Stenius, U. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-303 · Cited by 30 · see citing works
Woller, B., Daw, A., Aston, V. T., Lloyd, J., Snow, G. L., Stevens, S. M., Woller, S. C., Jones, P., & Bledsoe, J. R. (2021) · Clinical and Applied Thrombosis/Hemostasis · doi:10.1177/10760296211013108 · Cited by 30 · see citing works
Mascio, A., Stewart, R. J., Botelle, R., Williams, M., Mirza, L., Patel, R., Pollak, T. A., Dobson, R., & Roberts, A. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.711941 · Cited by 27 · see citing works
Self-citationBio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine
Chiu, B., Pyysalo, S., Vulić, I., & Korhonen, A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2039-z · Cited by 26 · see citing works
Named Entity Recognition for Bacterial Type IV Secretion Systems
Ananiadou, S., Sullivan, D., Black, W. J., Levow, G.-A., Gillespie, J. J., Mao, C., Pyysalo, S., Kolluru, B., Tsujii, J., & Sobral, B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014780 · Cited by 24 · see citing works
Ramu, N., Kolliakou, A., Sanyal, J., Patel, R., & Stewart, R. J. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-028929 · Cited by 24 · see citing works
Self-citationNoor, K., Roguski, Ł., Bai, X., Handy, Á., Klapaukh, R., Folarin, A., Romão, L., Matteson, J., Lea, N., Zhu, L., et al. (2022) · JMIR Medical Informatics · doi:10.2196/38122 · Cited by 24 · see citing works
Self-citationCOMETA: A Corpus for Medical Entity Linking in the Social Media
Basaldella, M., Liu, F., Shareghi, E., & Collier, N. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.253 · Cited by 24 · see citing works
Self-citationDisagreement Between Clinicians and Score in Decision-Making Capacity of Critically Ill Patients*
Bertrand, P.-M., Pereira, B., Adda, M., Timsit, J., Wolff, M., Hilbert, G., Gruson, D., Garrouste-Orgeas, M., Argaud, L., Constantin, J., et al. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003550 · Cited by 23 · see citing works
Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data
Bean, D. M., Teo, J., Wu, H., Oliveira, R., Patel, R., Bendayan, R., Shah, A. M., Dobson, R., & Scott, P. A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0225625 · Cited by 23 · see citing works
Self-citationViani, N., Kam, J., Yin, L., Bittar, A., Dutta, R., Patel, R., Stewart, R. J., & Velupillai, S. U. (2020) · Journal of Biomedical Semantics · doi:10.1186/s13326-020-00220-2 · Cited by 23 · see citing works
Self-citationJones, K. H., Ford, E. M., Lea, N., Griffiths, L. J., Hassan, L., Heys, S., Squires, E., & Nenadić, G. (2020) · Journal of Medical Internet Research · doi:10.2196/16760 · Cited by 23 · see citing works
Adaptable, high recall, event extraction system with minimal configuration
Miwa, M., & Ananiadou, S. (2015) · BMC Bioinformatics · doi:10.1186/1471-2105-16-s10-s7 · Cited by 21 · see citing works
Text mining for improved exposure assessment
Larsson, K., Baker, S., Silins, I., Guo, Y., Stenius, U., Korhonen, A., & Berglund, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173132 · Cited by 21 · see citing works
Mu, T., Goulermas, J. Y., Korkontzelos, I., & Ananiadou, S. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23374 · Cited by 19 · see citing works
Fu, X., Batista-Navarro, R., Rak, R., & Ananiadou, S. (2015) · Journal of Biomedical Semantics · doi:10.1186/s13326-015-0004-6 · Cited by 19 · see citing works
Guo, Y., Korhonen, A., Liakata, M., Silins, I., Högberg, J., & Stenius, U. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-69 · Cited by 19 · see citing works
Self-Alignment Pretraining for Biomedical Entity RepresentationsPreprint
Liu, F., Shareghi, E., Meng, Z., Basaldella, M., & Collier, N. (2020) · arXiv (Cornell University) · doi:10.17863/cam.72095 · Cited by 19 · see citing works
Self-citationLimsopatham, N., & Collier, N. (2016) · Workshop on Biomedical Natural Language Processing (BioNLP) · doi:10.18653/v1/w16-2918 · Cited by 18 · see citing works
Self-citationShah, A. D., Bailey, E., Williams, T. J., Denaxas, S., Dobson, R., & Hemingway, H. (2019) · Journal of Biomedical Semantics · doi:10.1186/s13326-019-0214-4 · Cited by 18 · see citing works
Self-citationPatel, R., Smeraldi, F., Abdollahyan, M., Irving, J., & Bessant, C. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-056601 · Cited by 18 · see citing works
Predicting high-cost care in a mental health setting
Colling, C., Khondoker, M. R., Patel, R., Fok, M. L.-Y., Harland, R., Broadbent, M. T. M., McCrone, P., & Stewart, R. J. (2020) · BJPsych Open · doi:10.1192/bjo.2019.96 · Cited by 17 · see citing works
Self-citationJackson, R., Patel, R., Velupillai, S. U., Gkotsis, G., Hoyle, D. C., & Stewart, R. J. (2018) · F1000Research · doi:10.12688/f1000research.13830.2 · Cited by 16 · see citing works
Self-citationAutomatic extraction of angiogenesis bioprocess from text
Wang, X., McKendrick, I., Barrett, I. P., Dix, I., French, T., Tsujii, J., & Ananiadou, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr460 · Cited by 15 · see citing works
Argo: enabling the development of bespoke workflows and services for disease annotation
Batista-Navarro, R., Carter, J., & Ananiadou, S. (2016) · Database · doi:10.1093/database/baw066 · Cited by 15 · see citing works
Alnazzawi, N., Thompson, P., & Ananiadou, S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162287 · Cited by 15 · see citing works
Wu, H., Hodgson, K., Dyson, S., Morley, K. I., Ibrahim, Z. M., Iqbal, E., Stewart, R. J., Dobson, R., & Sudlow, C. L. M. (2019) · JMIR Medical Informatics · doi:10.2196/14782 · Cited by 15 · see citing works
Self-citationA Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations
Bollegala, D., Kontonatsios, G., & Ananiadou, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126196 · Cited by 14 · see citing works
Anatomical Entity Recognition with a Hierarchical Framework Augmented by External Resources
Xu, Y. L., Ji, H., Ni, Z., Chen, Q., Fan, Y., Ananiadou, S., Chang, E., & Tsujii, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108396 · Cited by 14 · see citing works
Le, H.-Q., Tran, M.-V., Dang, T. H., Ha, Q.-T., & Collier, N. (2016) · Database · doi:10.1093/database/baw102 · Cited by 14 · see citing works
Self-citationSynthetic data for annotation and extraction of family history information from clinical text
Brekke, P., Rama, T., Pilán, I., Øystein Nytrø, A., & Øvrelid, L. (2021) · Journal of Biomedical Semantics · doi:10.1186/s13326-021-00244-2 · Cited by 14 · see citing works
Ali, I., Guo, Y., Silins, I., Högberg, J., Stenius, U., & Korhonen, A. (2015) · Toxicology Letters · doi:10.1016/j.toxlet.2015.11.003 · Cited by 13 · see citing works
Time Expressions in Mental Health Records for Symptom Onset Extraction
Viani, N., Yin, L., Kam, J., Alawi, A., Bittar, A., Dutta, R., Patel, R., Stewart, R. J., & Velupillai, S. U. (2018) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/w18-5621 · Cited by 13 · see citing works
Self-citationAnnotating Temporal Relations to Determine the Onset of Psychosis Symptoms
Viani, N., Kam, J., Yin, L., Verma, S., Stewart, R. J., Patel, R., & Velupillai, S. U. (2019) · Studies in health technology and informatics · doi:10.3233/shti190255 · Cited by 13 · see citing works
Self-citationDeveloping automated methods for disease subtyping in UK Biobank: an exemplar study on stroke
Rannikmäe, K., Wu, H., Tominey, S., Whiteley, W. N., Allen, N. E., Sudlow, C. L. M., & Biobank, T. U. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01556-0 · Cited by 13 · see citing works
Self-citationProcessing biological literature with customizable Web services supporting interoperable formats
Rak, R., Batista-Navarro, R., Carter, J., Rowley, A. G. D., & Ananiadou, S. (2014) · Database · doi:10.1093/database/bau064 · Cited by 12 · see citing works
A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation.
Singh, G., Marshall, I. J., Thomas, J., Shawe-Taylor, J., & Wallace, B. C. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132989 · Cited by 11 · see citing works
A neural classification method for supporting the creation of BioVerbNet
Chiu, B., Majewska, O., Pyysalo, S., Wey, L. T., Stenius, U., Korhonen, A., & Palmer, M. S. (2019) · Journal of Biomedical Semantics · doi:10.1186/s13326-018-0193-x · Cited by 11 · see citing works
McDonald, K. C., Smith, T. D., Broadbent, M. T. M., Patel, R., Geddes, J. R., & Saunders, K. E. A. (2020) · European Psychiatry · doi:10.1192/j.eurpsy.2020.39 · Cited by 11 · see citing works
BioReddit: Word Embeddings for User-Generated Biomedical NLP
Basaldella, M., & Collier, N. (2019) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/d19-6205 · Cited by 10 · see citing works
Self-citationGenerating Positive Psychosis Symptom Keywords from Electronic Health Records
Viani, N., Patel, R., Stewart, R. J., & Velupillai, S. U. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21642-9_38 · Cited by 9 · see citing works
Self-citationPatient discussions of glucocorticoid-related side effects within an online health community forum
Vivekanantham, A., Belousov, M., Hassan, L., Nenadić, G., & Dixon, W. G. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2019-216791 · Cited by 9 · see citing works
Bhavsar, V., Sanyal, J., Patel, R., Shetty, H., Velupillai, S. U., Stewart, R. J., Broadbent, M. T. M., MacCabe, J. H., Das‐Munshi, J., & Howard, L. M. (2020) · BJPsych Open · doi:10.1192/bjo.2020.52 · Cited by 9 · see citing works
Self-citationGenerating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models
Prokhorov, V., Pilehvar, M. T., & Collier, N. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1196 · Cited by 8 · see citing works
Self-citationKorkontzelos, I., & Ananiadou, S. (2014) · doi:10.1093/oxfordhb/9780199573691.013.004 · Cited by 8 · see citing works
Singh, G., Sabet, Z., Shawe-Taylor, J., & Thomas, J. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-53352-6_12 · Cited by 7 · see citing works
Soto, A. J., Zerva, C., Batista-Navarro, R., & Ananiadou, S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx774 · Cited by 6 · see citing works
Auto-CORPus: A Natural Language Processing Tool for Standardising and Reusing Biomedical LiteraturePreprint
Beck, T., Shorter, T., Hu, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.08.425887 · Cited by 5 · see citing works
Elkaref, M., & Hassan, L. (2021) · Social Media Mining for Health Research and Applications Workshop and Shared Tasks (SMM4H) · doi:10.18653/v1/2021.smm4h-1.16 · Cited by 5 · see citing works
Investigating Comorbidity of Mental and Physical Disorders in Online Health Forums
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EXTRACTING SECONDARY BIO-EVENT ARGUMENTS WITH EXTRACTION CONSTRAINTS
Sasaki, Y., Wang, X., & Ananiadou, S. (2011) · Computational Intelligence · doi:10.1111/j.1467-8640.2011.00406.x · Cited by 1 · see citing works
PUblications Metadata Augmentation (PUMA) pipeline
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- Authors
- Wu, H., Wang, M., Wu, J., Francis, F., Chang, Y.-H., Shavick, A., Dong, H., Poon, M. T. C., Fitzpatrick, N., Levine, A. P., Slater, K. T., Handy, A., Karwath, A., Gkoutos, G. V., Chelala, C., Shah, A. D., Stewart, R., Collier, N., Alex, B., Whiteley, W., Sudlow, C., Roberts, A., & Dobson, R. J. B.
- Affiliations
- University College London (GB)
- University of Edinburgh (GB)
- University of Oxford (GB)
- University of Birmingham (GB)
- Queen Mary University of London (GB)
- King's College London (GB)
- University of Cambridge (GB)
- Corresponding author
- Wu, Honghan
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2022-12-21
- Volume
- 5
- Issue
- 1
- Pages
- 186-186
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9770568
- OpenAlex
- W4312113143
- Cited by
- 101
- References
- 157
- Citations by year
- 2026: 29 · 2025: 40 · 2024: 18 · 2023: 13 · 2022: 1
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- DH | National Institute for Health Research (NIHR202639)
- British Council (UCL-NMU-SEU International Collaboration)
- University of Edinburgh (The Advanced Care Research Centre Programme)
- RCUK | Medical Research Council (MR/W014386, National Text Analytics Project)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- clinical natural language processing, clinical research funding, research funding trends, disease phenotyping, clinical text mining, research collaboration networks, academia-industry collaboration, United Kingdom
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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