REVIEW article
Front. Public Health
Sec. Digital Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1633487
This article is part of the Research TopicClinical prediction models in cancer through bioinformaticsView all 15 articles
Applications and Challenges of Biomarker-Based Predictive Models in Proactive Health Management
Provisionally accepted
- 1Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- 2School of Public Health and Health Management, Gannan Medical University, Ganzhou, China
- 3The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
- 4School of Basic Medicine, Gannan Medical University, Ganzhou, China
- 5Ganzhou Key Laboratory of Medical Big Data, Ganzhou, China
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Digital technology and artificial intelligence have revolutionized predictive models based on clinical data, creating opportunities for proactive health management. This review systematically evaluates the role and effectiveness of biomarker-driven predictive models across disease detection, personalized intervention, and healthcare resource optimization. Critical challenges hindering their implementation include data heterogeneity, inconsistent standardization protocols, limited generalizability across populations, high implementation costs, and substantial barriers in clinical translation. To address these challenges, we propose an integrated framework prioritizing three pillars: multi-modal data fusion, standardized governance protocols, and interpretability enhancement, systematically addressing implementation barriers from data heterogeneity to clinical adoption. This systematic approach enhances early disease screening accuracy while supporting risk stratification and precision diagnosis, particularly for chronic conditions and oncology applications. By effectively connecting biomarker discovery with practical clinical utilization, our proposed framework offers actionable methodologies that address existing limitations while guiding multidisciplinary research teams. Moving forward, expanding these predictive models to rare diseases, incorporating dynamic health indicators, strengthening integrative multi-omics approaches, conducting longitudinal cohort studies, and leveraging edge computing solutions for low-resource settings emerge as critical areas requiring innovation and exploration.
Keywords: Proactive health management, biomarkers, predictive models, Data heterogeneity； Public health resources, Multi-omics integration
Received: 22 May 2025; Accepted: 07 Aug 2025.
Copyright: © 2025 Zhao, Zhang, Zhang, Zhang, Liu and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: You Guo, Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.