Natural language processing models for predicting treatment outcomes in internet-delivered cognitive behavioral therapy.

ABSTRACT:

Objective

Predicting treatment outcome has the potential to enhance Internet-delivered Cognitive Behavioral Therapy (ICBT). One aspect of guided ICBT is patient-therapist interaction through written messages. With Natural language processing (NLP) these could be leveraged to predict outcome; however current evidence is limited. This study investigates the predictive accuracy of NLP models for treatment outcomes and evaluates whether NLP provides additional predictive value beyond symptom variables.

Methods

Patient-therapist messages from 6613 patients undergoing 12 weeks of treatment were used to train three types of NLP models: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from transformers (BERT), and BERT for Longer Text (BELT).

SUBMITTER: Hentati Isacsson N 

PROVIDER: S-EPMC12547759 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature