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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pretrained language models for semantics-aware data harmonisation of observational clinical studies in the era of big data

Jakub Dylag, Michael Boniface, Zlatko Zlatev
1 citations
BMC Medical Informatics and Decision Making
Machine Learning in Healthcare
2.48
article

Pretrained language models for semantics-aware data harmonisation of observational clinical studies in the era of big data

Jakub Dylag, Michael Boniface, Zlatko Zlatev
article en
1 citations

Abstract

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.

BMC Medical Informatics and Decision MakingVol. 25(1)
NIHR Southampton Biomedical Research Centre (GB), University of Southampton (GB)
National Institute for Health and Care Research, Department of Health and Social Care
Openalex Percentile: Top 8%
Machine Learning in Healthcare
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.