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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Health Data Science Database of Beijing Friendship Hospital |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No intervention will be applied. | Other | This is an observational study. |
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| 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. |
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Inclusion Criteria:
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.
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Health Data Science Database of Beijing Friendship Hospital
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beijing Friendship Hospital, Capital Medical University | Beijing | Beijing Municipality | 100050 | China |
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