Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health
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 cance...
| Authors | Jingwei Huang, Kuroush Nezafati, Zhikai Chi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 March 2026 |
| Last updated at source | 17 April 2026 |
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