@inproceedings{lv-etal-2026-dimas,
    title = "{DIMAS}-{OMOP}: A Deliberative Intelligence-Based Multi-Agent System for {C}hinese Medical Text Standardization toward {OMOP}",
    author = "Lv, Hanlin  and
      Wang, Xiao  and
      Wu, Kesong  and
      Li, Lei  and
      Wang, Lei",
    editor = "Huang, Kaiyu  and
      Mo, Fengran  and
      Chen, Pinzhen  and
      Jiang, Meng",
    booktitle = "Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models ({M}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.mellm-1.10/",
    pages = "108--118",
    ISBN = "979-8-89176-430-9",
    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 six-agent 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."
}