Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for…

AI / informaticsJournal of medical Internet research· Aug 2026

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.

Olly Kravchenko, Matthew Halvorsen, Julia Bäckman … John Wallert

Mood DisordersAI / informaticsQ1IF 8.5

Machine learning model predicted clinically meaningful improvement after internet-delivered CBT with AUC 0.749.

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.

Olly Kravchenko … John Wallert

Journal of medical Internet research · Aug 2026

Background

Up to 50% of patients treated with internet-delivered cognitive behavioral therapy (ICBT) for depression and anxiety disorders do not experience clinically significant symptom reduction.

Purpose

The aim of this study was to enhance baseline prediction of clinically meaningful improvement in patients treated with ICBT for common psychiatric disorders in routine care, which could ultimately inform treatment planning at intake.

Methods

We developed multimodal predictive models integrating clinical, sociodemographic, and genetic data available pretreatment to predict clinically meaningful improvement in a sample of 1790 patients treated with ICBT for major depressive disorder, panic disorder, and social anxiety disorder.

n = 1790 patients

Results

model correctly separated better vs worse ICBT outcomes most of the time

AUC 0.75 (95% CI 0.70 to 0.80)

null 0

0.70

0.80

CI excludes the null - significant

More results

Compared with the benchmark model based on self-reported screening data (AUCtest 0.695, 95% CI 0.637-0.748), RF and both ensemble models incorporating register data showed higher discrimination in paired DeLong tests (P=.04, P=.02, and P=.03, respectively), whereas polygenic scores added no independent predictive value in this cohort and modeling setup (P=.97).

“

Conclusion

These promising results support the feasibility of baseline prognostic prediction of clinically meaningful improvement after ICBT and provide a basis for the prospective validation of model-informed risk stratification.

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.

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