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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16968383 |
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Table of Contents:
- <p><span>Predictive models for cardiovascular disease (CVD) hold immense promise for improving early intervention and personalized patient care. However, their real-world clinical utility is critically limited by pervasive issues of data bias and poor generalizability. Many models are developed on datasets that are not representative of the broader population, often suffering from selection bias, measurement bias, and underrepresentation of minority racial, ethnic, socioeconomic, and gender groups. This leads to models that perpetuate existing health disparities and perform suboptimally when deployed in diverse clinical settings. This paper examines the sources and impacts of data bias in CVD prediction, including the use of electronic health records (EHRs) and legacy risk factors that may not be universally applicable. We explore methodological strategies to mitigate these issues, such as employing algorithmic fairness techniques, utilizing diverse and representative cohort data, applying transfer learning and domain adaptation methods, and rigorous external validation across heterogeneous populations. We argue that moving beyond mere statistical accuracy to prioritize equity and generalizability is not just an technical challenge but an ethical imperative. The development of robust, fair, and universally applicable predictive CVD models is essential for equitable healthcare advancement and requires a concerted effort toward inclusive data collection and transparent model reporting.</span></p>