Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 7, 2025
Open Peer Review Period: Apr 17, 2025 - Jun 12, 2025
Date Accepted: Jun 14, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Automated Data Harmonization in Clinical Research: Natural Language Processing Approach
ABSTRACT
Background:
Integrating data is essential for advancing clinical and epidemiological research. However, the process of integrating and harmonizing variables from research studies remains a major bottleneck.
Objective:
The objective was to assess a natural language processing (NLP)-based method to automate variable harmonization to achieve a scalable approach to integration of multiple datasets.
Methods:
We developed a fully connected neural network method, enhanced with contrastive learning, using domain-specific embeddings from BioBERT using three cardiovascular datasets: Atherosclerosis Risk in Communities (ARIC) study, the Framingham Heart Study (FHS) and the Multi-Ethnic Study of Atherosclersosis (MESA) . We used meta-data variable descriptions and curated harmonized concepts as ground truth. The accuracy of this method was compared to a logistic regression baseline method.
Results:
The newly developed fully connected neural network achieved a top-5 accuracy of 98.95% (95% CI: 98.31%-99.47%) and an AUC of 0.990 (95% CI: 0.988-0.991), outperforming the accuracy of to the standard logistic regression model.
Conclusions:
This novel approach provides a scalable solution for harmonizing meta-data across large-scale cohort studies.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.