A Multidimensional Framework for Data Quality Assessment in Heart Failure: Integrating IEEE 2801-2022 and Fairness Metrics

Heart failure (HF) affects over 64 million people globally and poses complex diagnostic and therapeutic challenges. Reliable clinical research in HF hinges on high-quality data. This study presents a novel data quality assessment (DQA) framework tailored to retrospective HF datasets. It adapts the IEEE standard 2801-2022 criteria—originally for general medical data—to HF's clinical and multimodal structure and introduces a fairness-aware dimension to assess demographic representativeness. Applied to a real-world dataset of 6,039 patients and over 110,000 records across 11 clinical domains, the framework evaluates six dimensions: Completeness, Accuracy, Consistency, Compliance, Timeliness, and Fairness. Initial completeness was low <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(48.82 \%)$</tex>, but improved to 61.04% after cleaning via outlier correction, imputation, and schema normalization. Accuracy and compliance reached 100%, and consistency improved to 99.61%. Fairness, measured via JensenShannon Similarity across age, sex, and BMI, remained at 87.35%, highlighting demographic imbalance remained unresolved by technical cleaning. This is the first standards-aligned, domain-adapted, and fairness-extended DQA pipeline for HF, producing a robust dataset suitable for machine learning and clinical decision support.

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Published
2025-11-06
DOI
https://doi.org/10.1109/bibe66822.2025.00082
Primary Topic
Machine Learning in Healthcare
Type
article
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article

A Multidimensional Framework for Data Quality Assessment in Heart Failure: Integrating IEEE 2801-2022 and Fairness Metrics

Fausto Barlocco, Konstantina-Helen Tsarapatsani, Matej Pičulin, Dimitrios G. Boucharas et al.
Machine Learning in Healthcare
article

A Multidimensional Framework for Data Quality Assessment in Heart Failure: Integrating IEEE 2801-2022 and Fairness Metrics

Fausto Barlocco, Konstantina-Helen Tsarapatsani, Matej Pičulin, Dimitrios G. Boucharas, Iacopo Olivotto, Djordje G. Jakovljević, Sarah J. Charman, Marta Jiménez-Blanco, Frans Hendrik Rutten, Zoran Bosnić, Dimitrios Manousos, Dimitrios I. Fotiadis, Amy Groenewegen, Prithwish Banerjee, Nduka C. Okwose, Manolis Tsiknakis, Lazar Velicki, Lars Siegfried Maier, Maria Tafelmeier, Andrej Preveden, Duncan Edwards, José Luis Zamorano, M. Georgoula, Grigorios G. Kotoulas, Ioannis Kyprakis, Borut Flis, Peter Vračar
article en

Abstract

Heart failure (HF) affects over 64 million people globally and poses complex diagnostic and therapeutic challenges. Reliable clinical research in HF hinges on high-quality data. This study presents a novel data quality assessment (DQA) framework tailored to retrospective HF datasets. It adapts the IEEE standard 2801-2022 criteria—originally for general medical data—to HF's clinical and multimodal structure and introduces a fairness-aware dimension to assess demographic representativeness. Applied to a real-world dataset of 6,039 patients and over 110,000 records across 11 clinical domains, the framework evaluates six dimensions: Completeness, Accuracy, Consistency, Compliance, Timeliness, and Fairness. Initial completeness was low <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(48.82 \%)$</tex>, but improved to 61.04% after cleaning via outlier correction, imputation, and schema normalization. Accuracy and compliance reached 100%, and consistency improved to 99.61%. Fairness, measured via JensenShannon Similarity across age, sex, and BMI, remained at 87.35%, highlighting demographic imbalance remained unresolved by technical cleaning. This is the first standards-aligned, domain-adapted, and fairness-extended DQA pipeline for HF, producing a robust dataset suitable for machine learning and clinical decision support.

University of Ljubljana (SI), University of Novi Sad (RS), Instituto Cajal (ES), University of Cambridge (GB), University Medical Center Utrecht (NL), University Hospital Regensburg (DE), Azienda Ospedaliero-Universitaria Careggi (IT), FORTH Institute of Computer Science (GR), FORTH Institute of Electronic Structure and Laser (GR), Primary Health Care (QA), Hospital Universitario Ramón y Cajal (ES), University of Florence (IT), Coventry University (GB), Newcastle University (GB)
Openalex Percentile: Top 79%
Machine Learning in Healthcare
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