NCT06350201

Machine Learning-based Classification of Symptom Clusters and Online CBT

Machine Learning-based Classification of Symptom Clusters and Matched Online Cognitive Behavior Intervention for Depression Symptom and Anxiety Symptom

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 380 (estimated)
• Sponsor: Wuhan Mental Health Centre · Academic / Other
• Sex: All
• Age: 18 Years – 64 Years
• Healthy volunteers: Not accepted

Summary

To breakthrough the bottleneck identified, we will conduct a cross-sectional study to develop a symptom clustering model for depression and anxiety. A wide range of statistical methods as well as machine learning approaches were explored, and a cohesive hierarchical clustering algorithm will be used. After developing the model, a symptom-matched intervention program based on problem solving therapy will be formulated. We are supposed to examine whether its use for personalizing symptom-matched psychological treatment can lead to improved patient outcomes, compared with usual care. This project is expected to provide a new and precise method for the emotion management, which will provide a standardized intervention pathway combining screening with treatment for the management of depression symptom and anxiety symptom. A preciser intervention matched to individual symptoms may provide important insight in improving patient outcome as well as a standardized mood management pathway targeting to the early detection and intervention for community residents.

Conditions

• Depression and Anxiety Symptom

Interventions

Type	Name	Description
BEHAVIORAL	problem solving therapy	Problem-solving therapy-based holistic emotion management interventions matched to individual symptoms
OTHER	control group	Routine psychological care and guidance on mood management

Timeline

• Start date: 2025-09-01
• Primary completion: 2026-05-01
• Completion: 2026-12-01
• First posted: 2024-04-05
• Last updated: 2026-01-08

Locations

1 site across 1 country: China