From Documents to Spans: Span-Centric Learning for LLM-based Explainable Medical Coding
The authors propose Span-Centric Learning (SCL), a method to improve Large Language Models' ability to perform medical ICD coding from long clinical documents. SCL learns coding knowledge from short text segments (spans) and then applies this knowledge to full documents. This approach requires less training data and provides explicit supporting evidence for every predicted code, making the process more accurate and auditable.