Evidence map›Paper›PMID 40900695›Full record

SynthesisFrontiers in public health2025

Applications and challenges of biomarker-based predictive models in proactive health management.

Qiming Zhao, Chen Zhang, Wanxin Zhang, Shengchuan Zhang, Qiyuan Liu, You Guo

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Beyond the gut microbe: Rethinking infection, microbiome harmony, and the one-health continuum.World journal of gastrointestinal pharmacology and therapeutics · 2026
    Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Programming the tumor microenvironment through microbiome-driven mechanisms.Frontiers in cellular and infection microbiology · 2026
    Review
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Qiming ZhaoMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Chen ZhangMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Wanxin ZhangThe First School of Clinical Medicine, Gannan Medical University, Ganzhou, China.
Shengchuan ZhangSchool of Basic Medicine, Gannan Medical University, Ganzhou, China.
Qiyuan LiuThe First School of Clinical Medicine, Gannan Medical University, Ganzhou, China.
You GuoMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BiomarkersArtificial IntelligenceHumansBiomarkersbiomarkersdata heterogeneitymulti-omics integrationpredictive modelsproactive health managementpublic health resources

Identifiers

PMID40900695
PMCPMC12399543

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.