Academic

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

arXiv:2603.20435v1 Announce Type: new 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 across four numeric variables from 0.806 to 0.895; (2) On Ewing

J
Jingwei Huang, Kuroush Nezafati, Zhikai Chi, Ruichen Rong, Colin Treager, Tingyi Wanyan, Yueshuang Xu, Xiaowei Zhan, Patrick Leavey, Guanghua Xiao, Wenqi Shi, Yang Xie · · 1 min read · 2 views
#cs.AI

arXiv:2603.20435v1 Announce Type: new 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 across four numeric variables from 0.806 to 0.895; (2) On Ewing sarcoma CD99 immunostaining pattern identification (n=200), the accuracy improved from 0.870 to 0.927; (3) On lung cancer tumor staging (n=100), tumor stage accuracy improved from 0.680 to 0.833 (pT: 0.842 -> 0.884; pN: 0.885 -> 0.948). The results demonstrate that deep reflective reasoning can systematically improve the reliability of LLM-based structured data extraction under interdependence constraints, enabling more consistent machine-operable clinical datasets and facilitating knowledge discovery with machine learning and data science towards digital health.

Executive Summary

This article proposes a novel framework, deep reflective reasoning, to enhance the accuracy of structured data extraction from clinical notes using Large Language Models (LLMs). The framework iteratively self-critiques and revises structured outputs by checking consistency among variables, the input text, and retrieved domain knowledge, stopping when outputs converge. The authors evaluate the proposed method in three diverse oncology applications, demonstrating significant improvements in accuracy and reliability. The results highlight the potential of deep reflective reasoning to facilitate knowledge discovery and machine learning in digital health. The framework's ability to navigate interdependent variables and capture subtle relationships in clinical data is a significant advancement in clinical informatics.

Key Points

  • ▸ Deep reflective reasoning is a novel framework for structured data extraction from clinical notes using LLMs.
  • ▸ The framework iteratively self-critiques and revises structured outputs to improve accuracy and reliability.
  • ▸ The authors evaluate the proposed method in three diverse oncology applications, demonstrating significant improvements in accuracy.

Merits

Strength in addressing interdependence constraints

The deep reflective reasoning framework effectively navigates interdependent variables in clinical data, capturing subtle relationships and improving accuracy.

Improved reliability and consistency

The iterative self-critique and revision process ensures that outputs are consistent and reliable, reducing the risk of clinically inconsistent results.

Demerits

Limited evaluation in diverse clinical domains

The article focuses on oncology applications and may not generalize to other clinical domains, highlighting the need for further evaluation and validation.

Potential computational complexity

The iterative self-critique and revision process may introduce computational complexity, potentially limiting the scalability of the framework in large-scale clinical data extraction.

Expert Commentary

The deep reflective reasoning framework is a significant advancement in clinical informatics and machine learning. Its ability to navigate interdependent variables and capture subtle relationships in clinical data has the potential to revolutionize the field of clinical informatics. However, further evaluation and validation across diverse clinical domains are necessary to ensure the framework's generalizability. Additionally, the potential computational complexity of the framework requires careful consideration to ensure scalability in large-scale clinical data extraction.

Recommendations

  • ✓ Further evaluation and validation of the deep reflective reasoning framework in diverse clinical domains and settings.
  • ✓ Investigation into the computational complexity of the framework and exploration of strategies to mitigate potential scalability limitations.

Sources

Original: arXiv - cs.AI

Related Articles