NCT07375303

Data Mining of Population Health-sub-health-disease Based on Dynamic System Theory

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 380,000 (estimated)
• Sponsor: Beijing Friendship Hospital · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

This study aims to explore the dynamic evolution patterns of population health, sub-health, and disease states through dynamic system theory and big data mining methods, providing scientific evidence for personalized prevention and health management.

Detailed description

Specific objectives include: (1) Identifying individual health, sub-health, and disease states using unsupervised system modeling techniques, while investigating their mutual transformation pathways. (2) Identifying key indicators determining state transitions, clarifying their mechanisms and interactions. (3) Developing dynamic system models to simulate state transition trajectories under multivariate influences, predicting individual probabilities of progression from health to sub-health or disease. (4) Creating interpretable health prediction tools based on modeling results to support precision interventions. The ultimate goal is to establish a scientifically validated yet implementable health state modeling system, offering quantifiable tools for early intervention and personalized health management to reduce chronic disease incidence and healthcare burdens.

Conditions

• Sub-healthy

Interventions

Type	Name	Description
OTHER	No intervention will be applied.	This is an observational study.

Timeline

• Start date: 2025-09-01
• Primary completion: 2028-08-31
• Completion: 2030-08-31
• First posted: 2026-01-29
• Last updated: 2026-01-29

Locations

1 site across 1 country: China