Abstract

Background 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 efficiency of healthcare delivery. Yet, realising these potential demands requires substantial efforts in harmonising source datasets and curating study data, which currently relies on costly and time-consuming manual and labour-intensive methods.

Objectives We evaluate the applicability of AI methods for natural language processing (NLP) and unsupervised machine learning (ML) to 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.

Methods We assess NLP and unsupervised ML algorithms and propose two 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.

Results 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 leading implementations in other relevant domains. Automation can significantly accelerate the process of dataset harmonization. Manual labelling was performed at a speed of 2.1 descriptions per minute, with our automated labelling increasing speed to 245 descriptions per minute.

Conclusions 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, accelerating insights from data for clinical benefit.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This report is independent research funded by the National Institute for Health Research (Artificial Intelligence for Multiple Long-Term Conditions (AIM), The development and validation of population clusters for integrating health and social care: A mixed-methods study on Multiple Long-Term Conditions, NIHR202637). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.

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https://doi.org/10.5255/UKDA-Series-200011

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AbbreviationsAIArtificial IntelligenceBERTBidirectional Encoder Representations from TransformersDBSCANdensity-based spatial clustering of applications with noiseEHRElectronic Healthcare RecordsELSAEnglish Longitudinal Study of AgeingHSEHealth Survey for EnglandHACHierarchal Agglomerative ClusteringHDBSCANHierarchal density-based spatial clustering of applications with noiseICUIntensive Care UnitsMLMachine LearningMAPMean Average PrecisionMMVMean Max V-MeasureMLTCMultiple Long-Term ConditionsNLPNatural Language ProcessingPCAPrincipal Component AnalysisSTSSemantic Textual SimilaritySBERTSentence-BERTSemDHPSemantic Data Harmonisation PipelineSNLIStanford Natural Language Inferencet-SNET-distributed stochastic neighbour embeddingsT5Text-to-Test Transfer TransformerUMLSUnified Medical Language SystemUMAPUniform Manifold Approximation and ProjectionUSEUniversal Sentence Encoders