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

NCT07375303 | Active Not Recruiting

• 1 other identifier: 202511
• Study Type: observational
• Target: 380,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Sub-healthy

Outcome Measures

Primary Outcomes (3)

• 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.

Study Arms (1)

Health Data Science Database of Beijing Friendship Hospital

1. 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. 2. 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%. 3. 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.

Interventions

No intervention will be applied. OTHER

This is an observational study.

Health Data Science Database of Beijing Friendship Hospital

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Health Data Science Database of Beijing Friendship Hospital

You may qualify if:

• 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%.

You may not qualify if:

• \- 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.

Sponsors & Collaborators

• Beijing Friendship Hospital: lead

Study Sites (1)

Beijing Friendship Hospital, Capital Medical University

Beijing, Beijing Municipality, 100050, China

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: December 9, 2025
• First Posted: January 29, 2026
• Study Start: September 1, 2025
• Primary Completion (Estimated): August 31, 2028
• Study Completion (Estimated): August 31, 2030
• Last Updated: January 29, 2026

Record last verified: 2026-01

Locations

CN (1)