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Contribution title Advancing Pediatric OCD Treatment with AI: Decoding Therapy Processes for ERP Success
Contribution code D2.004
Authors
  1. Nicoline Løcke Jepsen Korsbjerg Child and Adolescent Mental Health Center, Copenhagen University Hospital – Mental Health Services CPH, Copenhagen, Denmark Presenter
  2. Anne Katrine Pagsberg Child and Adolescent Mental Health Center, Copenhagen University Hospital
  3. Line Katrine Harder Clemmensen University of Copenhagen
  4. Nicole Nadine Lønfeldt Child and Adolescent Mental Health Center, Mental Health Services, Capital Region of Denmark
Form of presentation Poster
Topic
  • T01 - AI and digital health
Abstract Aims:
Machine learning (ML) and artificial intelligence (AI) hold great promise for advancing mental health care by improving therapy delivery through enhanced personalization and scalability. In the context of cognitive-behavioral therapy (CBT), particularly Exposure and Response Prevention (ERP) for pediatric obsessive-compulsive disorder (OCD), AI tools could help analyze therapy sessions, offering therapists valuable insights to better tailor care. To achieve this, a foundational understanding of the therapeutic processes driving successful outcomes is essential. This study aims to build that foundation by identifying and coding key behavioral and relational components of effective ERP.
Methods:
Videotaped ERP sessions from 64 children and adolescents aged 8–17 are being manually coded for critical therapeutic processes. The coding focuses on treatment-specific behaviors and relational dynamics between the child and therapist. These high-quality, human-coded datasets will serve as the foundation for train