[2603.20435] Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health

Computer Science > Artificial Intelligence arXiv:2603.20435 (cs) [Submitted on 20 Mar 2026] Title:Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health Authors:Jingwei Huang, Kuroush Nezafati, Zhikai Chi, Ruichen Rong, Colin Treager, Tingyi Wanyan, Yueshuang Xu, Xiaowei Zhan, Patrick Leavey, Guanghua Xiao, Wenqi Shi, Yang Xie View a PDF of the paper titled Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health, by Jingwei Huang and 11 other authors View PDF 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 revises structured outputs by checking consistency among variables, the input text, and retrieved domain knowledge, stopping when outputs converge. We extensively evaluate the proposed method in three diverse oncology applications: (1) On colorectal cancer synoptic reporting from gross descriptions (n=217), reflective reasoning improved average F1 across eight categorical synoptic variables from 0.828 to 0.911 and increased mean correct rate a...