Gregorio, J; Lemanska, A; Cieszynski, B; Peric, N; Alsuleman, M; Chrubasik, M; Duncan, P (2026) An EHR Data Standardisation Pipeline Using the MIMIC-III Dataset: Foundation for a Clinical Trial Data Quality Assessment:. In: 19th International Conference on Health Informatics, 2-4 March 2026, Marbella, Spain.

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Abstract

Demographic representativeness in clinical trials is essential to ensure treatments are applicable to all patients and that everyone can benefit equally. Clinical datasets, such as MIMIC-III, offer valuable opportunities for secondary research, but their fragmented structures and unstandardised content pose significant challenges for quality assessment. This paper presents a reproducible data processing pipeline designed to prepare electronic health record data for evaluating the demographic representativeness of clinical trial populations. The pipeline consolidates demographic data from multiple sources within the dataset and maps unstandardised diagnostic terms to ICD-11-compliant terms by using WHO APIs and an enhanced synonym dictionary of clinically relevant diagnostic terms to improve term-matching. It extracts disease-specific cohorts for validation. When applied to the dataset, the pipeline successfully combined patient data from multiple tables and achieved 93.92 % frequency-weighted diagnostic mapping coverage. A sepsis cohort was extracted to demonstrate the ability to generate well-characterised target populations with complete scoring across key demographic features. The resulting, unified, and standardised dataset supports the future development of objective quality scores that support transparent trial evaluation and informed decision-making. By aligning with FAIR data principles and established data quality frameworks, this work contributes to broader efforts in data governance and interoperability in clinical research

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Healthcare Data Standardisation, Clinical Trial Data, ICD-11 Mapping, Clinical Research, Data Processing, Pipeline, Health Informatics, Demographics Extraction, Data Quality, FAIR Data Principles.
Subjects:	Mathematics and Scientific Computing > Software Engineering
Divisions:	Data Science
Publisher:	SCITEPRESS - Science and Technology Publications
Identification number/DOI:	10.5220/0014219400004070
Last Modified:	14 Sep 2026 11:06
URI:	https://eprintspublications.npl.co.uk/id/eprint/10517