Pretrained language models for semantics-aware data harmonisation of observational clinical studies in the era of big data
Our study findings underscore the potential of AI technologies, such as NLP and unsupervised ML, in automating the harmonisation and curation of big data for clinical research. By establishing a robust technological foundation, we pave the way for the development of automated tools that streamline the process, enabling health data scientists to leverage big data more efficiently and effectively in their studies and accelerating insights from data for clinical benefit.
Authors
- Jakub Dylag (ORCID: https://orcid.org/0000-0001-6263-7339)
- Michael Boniface (ORCID: https://orcid.org/0000-0002-9281-6095)
- Zlatko Zlatev (ORCID: https://orcid.org/0000-0001-6275-5626)
Institutions
- NIHR Southampton Biomedical Research Centre (GB)
- University of Southampton (GB)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-10-29
- DOI
- https://doi.org/10.1186/s12911-025-03055-y
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- National Institute for Health and Care Research
- Department of Health and Social Care