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

In clinical research, there is a strong drive to leverage big data from population cohort studies and routine electronic healthcare records to design new interventions, improve health outcomes and increase the efficiency of healthcare delivery. However, realising these potential demands requires substantial efforts in harmonising source datasets and curating study data, which currently relies on costly, time-consuming and labour-intensive methods. We explore and assess the use of natural language processing (NLP) and unsupervised machine learning (ML) to address the challenges of big data semantic harmonisation and curation.

Our aim is to establish an efficient and robust technological foundation for the development of automated tools supporting data curation of large clinical datasets. We propose two AI based pipelines for automated semantic harmonisation: a pipeline for semantics-aware search for domain relevant variables and a pipeline for clustering of semantically similar variables. We evaluate pipeline performance using 94,037 textual variable descriptions from the English Longitudinal Study of Ageing (ELSA) database.

We observe high accuracy of our Semantic Search pipeline, with an AUC of 0.899 (SD = 0.056). Our semantic clustering pipeline achieves a V-measure of 0.237 (SD = 0.157), which is on par with that of leading implementations in other relevant domains. Automation can significantly accelerate the process of dataset harmonisation. Manual labelling was performed at a speed of 2.1 descriptions per minute, with our automated labelling increasing speed to 245 descriptions per minute.

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.