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AI-Based Phenome Data Analysis for Predicting the Onset of Major Diseases

AI-Based Phenome Data Analysis for Predicting the Onset of Major Diseases

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0011759
Enrollment
1000
Registered
2026-03-22
Start date
2026-04-02
Completion date
Unknown
Last updated
2026-04-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

Asan Medical Center
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Adults aged 30 to 60 years. 2. Disease group: Participants with a confirmed diagnosis of at least one of the following conditions: type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain. 3. Healthy control group: Participants with no prior diagnosis of type 2 diabetes mellitus, breast cancer, cardiovascular disease, osteoarthritis, or low back pain. 4. No history or current diagnosis of major medical conditions that may affect study outcomes, including but not limited to chronic kidney disease or liver cirrhosis. 5. Ability to understand the study procedures and provision of written informed consent prior to participation.

Exclusion criteria

Exclusion criteria: 1. Participants with incomplete or insufficient clinical or health screening data. 2. Participants considered inappropriate for study participation by the investigator.

Design outcomes

Primary

MeasureTime frame
Incident occurrence of each target disease within T years after the index date (binary outcome: yes/no) (Cardiovascular disease, Type 2 diabetes mellitus, Breast cancer, Low back pain, Osteoarthritis)

Secondary

MeasureTime frame
Discriminative performance of the artificial intelligence model in distinguishing between disease and control groups using baseline data from health screenings and clinical records (AUROC, PR-AUC);Diagnostic Performance Metrics (Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV)) ;Calibration Performance (Brier score, Calibration slope, Calibration intercept) ;Reclassification Performance Compared With Existing Risk Scores (if applicable) (Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), if applicable)

Countries

Korea, Republic of

Contacts

Public ContactEUN HA KIM

Asan Medical Center

sp98yuna@gmail.com+82-2-3010-8607

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Apr 17, 2026