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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Sub-healthy
• Intervention / Treatment: Other: No intervention will be applied.

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.

• Study Type: Observational
• Enrollment (Estimated): 380000

Contacts and Locations

Study Locations

• China
  • Beijing Municipality
    • Beijing, Beijing Municipality, China, 100050
      • Beijing Friendship Hospital, Capital Medical University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Health Data Science Database of Beijing Friendship Hospital

Description

Inclusion Criteria:

• Participants must have completed at least two consecutive physical examinations at the Physical Examination Center of Beijing Friendship Hospital, Capital Medical University, between June 2007 and August 2025, with a minimum interval of 6 months between adjacent records.
• Data records should be relatively complete, with missing rates for key research variables (e.g., core biochemical indicators, demographic information, and essential questionnaire items) ≤30%.
• Participants must have no prior history of severe organic diseases prior to their first study inclusion (as documented in medical records, primarily including: malignant tumors (non-curable/end-stage), severe cardiac insufficiency (NYHA Class III-IV), end-stage renal disease (CKD Stage 5), decompensated cirrhosis, or significant functional impairment caused by sequelae of severe cerebrovascular disease).

Exclusion Criteria:

- Individuals with a severe lack of basic data (such as unique identification, key demographic information, and core indicators of detection) or who cannot be effectively anonymized.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Health Data Science Database of Beijing Friendship Hospital; Participants must have completed at least two consecutive physical examinations at the Physical Examination Center of Beijing Friendship Hospital, Capital Medical University, between June 2007 and August 2025, with a minimum interval of 6 months between adjacent records.; Data records should be relatively complete, with missing rates for key research variables (e.g., core biochemical indicators, demographic information, and essential questionnaire items) ≤30%.; Participants must have no prior history of severe organic diseases prior to their first study inclusion (as documented in medical records, primarily including: malignant tumors (non-curable/end-stage), severe cardiac insufficiency (NYHA Class III-IV), end-stage renal disease (CKD Stage 5), decompensated cirrhosis, or significant functional impairment caused by sequelae of severe cerebrovascular disease).

Other: No intervention will be applied.; This is an observational study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Discriminative Accuracy for Next Diagnosis	Age- and Sex-stratified Area Under the Receiver Operating Characteristic Curve, AUC	Evaluate on an internal validation dataset. This dataset contains individual historical data up to January 1, 2018, based on which the model predicts the next diagnostic event that will occur immediately. Calculate AUC for diseases with over 1000 ICD-10
Long-term Predictive Accuracy	AUC stratified by age and gender, assessing the risk of disease occurrence within specific time intervals (1 year, 2 years,..., 10 years) after prediction. This indicator measures the decay of a model's predictive ability over time.	Evaluate the AUC values of disease occurrence in the 1st, 2nd, 3rd, 5th, and 10th year after prediction on the internal validation dataset.
Trajectory-level Predictive Accuracy	The proportion of correctly predicted disease events. In each simulated future year, match the generated disease events with the actual disease events that occur in individuals, and calculate the success rate (%) of the matching.	On the validation subset, evaluate the accuracy of disease event predictions for each year from the simulation starting point (60 years old) to the following 1 to 20 years.

Collaborators and Investigators

• Sponsor: Beijing Friendship Hospital

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2025
• Primary Completion (Estimated): August 31, 2028
• Study Completion (Estimated): August 31, 2030

Study Registration Dates

• First Submitted: December 9, 2025
• First Submitted That Met QC Criteria: January 24, 2026
• First Posted (Actual): January 29, 2026

Study Record Updates

• Last Update Posted (Actual): January 29, 2026
• Last Update Submitted That Met QC Criteria: January 24, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Other Study ID Numbers: 202511

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No