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

NCT05758285 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 6671

Last updated 2025-11-18

No results posted yet for this study

Summary

This study is to develop AI-based models for the personalised prediction of treatment engagement and treatment outcomes in patients engaging in digital psychotherapy. A large, real-world dataset of patients in a digital psychotherapy program will be used to train AI algorithms. Responsible AI algorithms will be developed by describing, accounting for, and mitigating bias due to severity of mental disturbances in AI-based models, in addition to considering bias due to other sensitive attributes, such as gender, ethnicity, and socio-demographic status.

Conditions

  • Mental Health Care
  • Mental 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).

Sponsors & Collaborators

  • University Hospital, Basel, Switzerland

    lead OTHER

Principal Investigators

  • Gunther Meinlschmidt, Prof. · University Hospital Basel, Department of Psychosomatic Medicine

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-03-01
Primary Completion
2024-12-06
Completion
2025-09-03

Countries

  • Switzerland

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05758285 on ClinicalTrials.gov