Advancing Pediatric Rehabilitation Documentation via Neuro-Symbolic AI

For automated documentation systems to be meaningful in pediatric rehabilitation, they must accurately capture and summarize information about a child's or youth's involvement in daily life activities. We present attention-ASP, a neuro-symbolic framework that combines Transformer attention with Answer Set Programming (ASP) to capture involvement-level information from interview-based text. By encoding domain-specific vocabularies as ASP programs, our model guides attention heads to focus on contextual cues such as place, time, activity, object, and people. Results show that symbolic reasoning over attention improves alignment with expert assessments, offering a promising direction for advancing clinical documentation tools in pediatric rehabilitation.