Machine Learning Model to Predict Assignment of Therapy Homework in Behavioral Treatments: Algorithm Development and
Gal Peretz1, C Barr Taylor2,3, Josef I Ruzek2,3
1Eleos Health, Waltham, MA, United States.
JMIR Formative Research
|May 15, 2023
Summary
A machine learning (ML) model accurately identified therapeutic homework in behavioral health sessions, improving objective tracking. This AI approach supports evidence-based care and can enhance treatment outcomes by analyzing homework completion in real-world settings.
Area of Science:
- Behavioral Health
- Artificial Intelligence
- Machine Learning
Background:
- Therapeutic homework is crucial for cognitive and behavioral interventions, yet its use in routine clinical care is understudied.
- Current research relies on self-reports, lacking objective data on homework in real-world therapy settings.
Purpose of the Study:
- To test a machine learning (ML) model using natural language processing (NLP) for identifying therapeutic homework in behavioral health sessions.
- To develop a more objective and accurate method for detecting homework assignments in therapy.
Main Methods:
- Analyzed 34,497 audio-recorded therapy sessions using an AI platform.
- Trained an unsupervised sentence embedding model on 2.83 million therapist-client microdialogues from 4000 sessions.
- Experts validated homework assignments in 100 sessions, achieving 90% agreement.
Main Results:
- Homework was assigned in 61% of sessions, with 34% having multiple assignments.
- The ML classifier achieved a 72% F1-score, outperforming existing models.
- Common homework types included practicing skills, taking action, journaling, and learning new skills.
Conclusions:
- ML and NLP can significantly improve the detection of therapeutic homework in behavioral health.
- AI can support therapists in real-world settings, enhancing evidence-based care and intervention fidelity.
- This approach can be extended to investigate homework's impact on therapeutic outcomes.
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