Using Responsible Artificial Intelligence (AI) to Predict Online Therapy Outcome and Engagement

Active, not recruitingOBSERVATIONAL
Enrollment

6,671

Participants

Timeline

Start Date

March 1, 2023

Primary Completion Date

December 6, 2024

Study Completion Date

December 31, 2025

Conditions
Mental Health CareMental Disorders
Interventions
OTHER

AI-Based Prediction of Treatment Engagement and Outcomes

AI-based algorithms and prediction models of treatment engagement and outcomes based on data from the Online Therapy Unit by Prof. Heather Hadjistavropoulos will be trained to predict symptom improvement of patients from pre- to post-digital psychotherapy intervention and to predict patients' engagement with the digital psychotherapy intervention and to predict patient drop out probability. For prediction model estimation, state of the art AI-based algorithms, such as XGBoost, is used . XGBoost is a machine learning method developed by refining previously established decision-tree-based methodologies. Data is split into training and testing sets (e.g., 80/20 split).

Trial Locations (1)

4031

University Hospital Basel, Department of Psychosomatic Medicine, Basel

All Listed Sponsors
lead

University Hospital, Basel, Switzerland

OTHER