Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health

arXiv:2603.20435v2 Announce Type: replace Abstract: Extracting structured information from clinical notes requires navigating a dense web of interdependent variables where the value of one attribute logically constrains others. Existing Large Language Model (LLM)-based extraction pipelines often struggle to capture these dependencies, leading to clinically inconsistent outputs. We propose deep reflective reasoning, a large language model agent framework that iteratively self-critiques and revise