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Advancing Pediatric Rehabilitation Documentation via Neuro-Symbolic AI
Esteban Guerrero1, Christine Imms2, Mats Granlund3
1Umeå University, Sweden.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study introduces attention-ASP, a new framework for automated clinical documentation in pediatric rehabilitation. It accurately captures children's daily activity involvement from interviews, improving documentation tools.
Area of Science:
- Artificial Intelligence
- Clinical Informatics
- Pediatric Rehabilitation
Background:
- Automated documentation systems are crucial for pediatric rehabilitation.
- Accurate capture of a child's daily life activity involvement is essential for meaningful documentation.
- Current systems may lack the nuance to capture this complex information.
Purpose of the Study:
- To present attention-ASP, a novel neuro-symbolic framework.
- To improve the accuracy of capturing involvement-level information from interview-based text in pediatric rehabilitation.
- To enhance automated clinical documentation tools.
Main Methods:
- Developed a neuro-symbolic framework combining Transformer attention with Answer Set Programming (ASP).
- Encoded domain-specific vocabularies as ASP programs.
- Guided attention heads to focus on contextual cues like place, time, activity, object, and people.
Main Results:
- The attention-ASP framework demonstrated improved alignment with expert assessments.
- Symbolic reasoning over attention enhanced the model's ability to capture involvement-level details.
- The approach shows promise for more accurate clinical documentation.
Conclusions:
- Attention-ASP offers a promising direction for advancing clinical documentation in pediatric rehabilitation.
- The neuro-symbolic approach effectively captures complex involvement information.
- This framework can lead to more meaningful and accurate documentation tools for children and youth.
