DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP
Year: 2026
Type: conference-paper
Abstract: Standardizing Chinese clinical imaging reports within the Observational Medical Outcomes Partnership (OMOP) framework is hindered by linguistic complexity and output inconsistency in existing methods.We propose DIMAS-OMOP, a Deliberative Intelligence-based Multi-Agent System designed for high-fidelity medical concept mapping toward OMOP standardization.Moving beyond single-model architectures, DIMAS-OMOP employs a hybrid three-stage workflow that integrates traditional natural language processing modules with selective Large Language Model reasoning and Retrieval-Augmented Generation.The core innovation lies in a hierarchical sixagent proposer-skeptic deliberation mechanism, complemented by a dynamic concept resolution approach and a four-dimensional quality control framework.Experimental results on 1,250 imaging reports demonstrate that DIMAS-OMOP achieves 95.2% mapping accuracy, significantly outperforming rule-based methods (+21.8 percentage points) and single-AI baselines (+8.1 percentage points).The system maintains a throughput of 1,200 reports/hour, with the multi-agent deliberation stage alone contributing an 8.9% relative accuracy gain.Furthermore, pilot deployment shows a 160.6% return on investment and a 31.5% increase in workflow efficiency.This study provides a novel, robust methodology for integrating unstructured non-English clinical data into the global Observational Health Data Sciences and Informatics (OHDSI) ecosystem through deliberative intelligence.
Authors: Hanlin Lv, Xiao Wang, Kesong Wu, Lei Li, Lei Wang
Institutions: The University of Queensland, Southeast University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7166855346