---
title: "DIMAS-OMOP — paper by Lv et al. (2026) — summary and ideas"
description: "Converting free-text Chinese radiology/imaging reports into the OMOP CDM v5.4 is difficult. 44 references."
authors: ["Hanlin Lv", "Xiao Wang", "Kesong Wu", "Lei Li", "Lei Wang"]
year: 2026
venue: "Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)"
source: "https://aclanthology.org/2026.mellm-1.10/"
doi: "10.18653/v1/2026.mellm-1.10"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lv-2026-dimas-omop-deliberative-intelligence-based/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lv-2026-dimas-omop-deliberative-intelligence-based/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/) / DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP

# DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP

**Authors:** Hanlin Lv, [Xiao Wang](https://md.research.elamur.ai/free/en/authors/xiao-wang/), Kesong Wu, [Lei Li](https://md.research.elamur.ai/free/en/authors/lei-li/) and [Lei Wang](https://md.research.elamur.ai/free/en/authors/lei-wang/) · **Year:** 2026 · **Venue:** [Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)](https://md.research.elamur.ai/free/en/venues/mellm/) ([Workshop on Multilinguality in the Era of Large Language Models 2026](https://md.research.elamur.ai/free/en/venues/mellm/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.mellm-1.10/) · [PDF](https://aclanthology.org/2026.mellm-1.10.pdf) · [DOI 10.18653/v1/2026.mellm-1.10](https://doi.org/10.18653/v1/2026.mellm-1.10) · [OpenAlex](https://openalex.org/W7166855346)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Converting free-text Chinese radiology/imaging reports into the OMOP CDM v5.4 is difficult. Rule-based tools break on abbreviations and phrasing variants, BERT-style extractors need costly expert annotation, and LLMs give inconsistent outputs or require cloud APIs that conflict with patient-privacy rules. The authors built DIMAS-OMOP, a locally deployed pipeline with three stages:
- deterministic NLP for normalization, NER and number extraction;
- on-demand calls to a local LLM with RAG, used only for ambiguous cases;
- a multi-agent debate in which a proposer suggests SNOMED CT concept mappings and a skeptic agent challenges them.

An audit agent then reviews the full debate. A four-dimensional quality gate must be passed before records are written to OMOP tables. Concept lookup relies on multi-strategy search with adaptive thresholds rather than hard-coded rules. On 1,250 reports from three tertiary hospitals, the system reached 95.2% mapping accuracy at about 1,200 reports per hour, clearly beating rule-based and single-model baselines. This matters because it shows a privacy-preserving route for bringing non-English clinical narratives into the OHDSI research ecosystem.

## Key points
- Six agents are coordinated by a task orchestrator: Linguist, Structurer, Mapping Proposer (ReAct), Skeptical Critic, Synthesis Audit and OMOP Builder. Output fills the OBSERVATION, MEASUREMENT and FACT_RELATIONSHIP tables of OMOP CDM v5.4 using SNOMED CT.
- Accuracy was 95.2%, which is +21.8 percentage points over rule-based methods and +8.1 over single-AI baselines. The deliberation stage alone gave an 8.9% relative improvement.
- In a pilot deployment, the authors report a 160.6% ROI and 31.5% higher workflow efficiency.
- Limitations noted by the authors:
  - a modest dataset from only three hospitals;
  - no inter-annotator agreement, held-out split details or error analysis reported;
  - usability feedback came mainly from informatics staff.
- Code is released at github.com/raywong2121/Multi-Agent, and data is available only on request. Planned extensions cover pathology, lab and operative reports, plus Japanese and Korean.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [The University of Queensland](https://md.research.elamur.ai/free/en/universities/the-university-of-queensland/) — University, Brisbane, Australia · 14 works · 4,603 citations
- [Southeast University](https://md.research.elamur.ai/free/en/universities/southeast-university-cn/) — University, Nanjing, China · 28 works · 354 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🧬 Biochemistry, Genetics and Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · 234 works · [🧬 Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/) · 231 works · [🩺 Medicine](https://md.research.elamur.ai/free/en/shelves/medicine/) · 241 works · [🩺 Health Informatics](https://md.research.elamur.ai/free/en/shelves/medicine/health-informatics/) · 184 works

## Cite

Cite as: Hanlin Lv, Xiao Wang, Kesong Wu, Lei Li and Lei Wang. 2026. DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP. Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{lv2026dimas,
  title = {{DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP}},
  author = {Hanlin Lv, Xiao Wang, Kesong Wu, Lei Li and Lei Wang},
  booktitle = {Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.mellm-1.10},
  url = {https://doi.org/10.18653/v1/2026.mellm-1.10}
}
```

- [← previous Digital Preservation of Aromanian Through Knowledge Management and Automatic Speech Recognition Evaluation](https://md.research.elamur.ai/free/en/papers/pendevska-2026-digital-preservation-aromanian-through-knowledge/)
- [next → DiNO: Disinformation Narrative Observer](https://md.research.elamur.ai/free/en/papers/sosnowski-2026-dino-disinformation-narrative-observer/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/)

## References

44 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019. [Publicly available clinical bert embeddings](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/). In Proceedings of the 2nd clinical natural language processing workshop, pages 72–78.
2. Alan R Aronson and François-Michel Lang. 2010. An overview of metamap: historical perspective and recent advances. Journal of the American medical informatics association, 17(3):229–236. [find](https://scholar.google.com/scholar?q=Alan+R+Aronson+and+Fran%C3%A7ois%2DMichel+Lang%2E+2010%2E+An+overview+of+metamap%3A+historical+perspective+and+recent+advances%2E+Journal+of+the+American+medical+informatics+association%2C+17%283%29%3A229%E2%80%93236%2E)
3. Adrien Bazoge, Emmanuel Morin, Béatrice Daille, and Pierre-Antoine Gourraud. 2023. Applying natural language processing to textual data from clinical data warehouses: systematic review. JMIR medical informatics, 11:e42477. [find](https://scholar.google.com/scholar?q=Adrien+Bazoge%2C+Emmanuel+Morin%2C+B%C3%A9atrice+Daille%2C+and+Pierre%2DAntoine+Gourraud%2E+2023%2E+Applying+natural+language+processing+to+textual+data+from+clinical+data+warehouses%3A+systematic+review%2E+JMIR+medical+i)
4. Olivier Bodenreider. 2004. The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research, 32(suppl_1):D267– D270. [find](https://scholar.google.com/scholar?q=Olivier+Bodenreider%2E+2004%2E+The+unified+medical+language+system+%28umls%29%3A+integrating+biomedical+terminology%2E+Nucleic+acids+research%2C+32%28suppl%5F1%29%3AD267%E2%80%93+D270%2E)
5. Xi Chen, Huahui Yi, Mingke You, WeiZhi Liu, Li Wang, Hairui Li, Xue Zhang, Yingman Guo, Lei Fan, Gang Chen, and 1 others. 2025. Enhancing diagnostic capability with multi-agents conversational large language models. NPJ digital medicine, 8(1):159. [find](https://scholar.google.com/scholar?q=Xi+Chen%2C+Huahui+Yi%2C+Mingke+You%2C+WeiZhi+Liu%2C+Li+Wang%2C+Hairui+Li%2C+Xue+Zhang%2C+Yingman+Guo%2C+Lei+Fan%2C+Gang+Chen%2C+and+1+others%2E+2025%2E+Enhancing+diagnostic+capability+with+multi%2Dagents+conversational+large+l)
6. Hannah Eyre, Alec B Chapman, Kelly S Peterson, Jianlin Shi, Patrick R Alba, Makoto M Jones, Tamara L Box, Scott L DuVall, and Olga V Patterson. 2022. Launching into clinical space with medspacy: a new clinical text processing toolkit in python. In AMIA Annual Symposium Proceedings, volume 2021, page… [find](https://scholar.google.com/scholar?q=Hannah+Eyre%2C+Alec+B+Chapman%2C+Kelly+S+Peterson%2C+Jianlin+Shi%2C+Patrick+R+Alba%2C+Makoto+M+Jones%2C+Tamara+L+Box%2C+Scott+L+DuVall%2C+and+Olga+V+Patterson%2E+2022%2E+Launching+into+clinical+space+with+medspacy%3A+a+new)
7. Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. 2018. Counterfactual multi-agent policy gradients. In Proceedings of the AAAI conference on artificial intelligence, volume 32. [find](https://scholar.google.com/scholar?q=Jakob+Foerster%2C+Gregory+Farquhar%2C+Triantafyllos+Afouras%2C+Nantas+Nardelli%2C+and+Shimon+Whiteson%2E+2018%2E+Counterfactual+multi%2Dagent+policy+gradients%2E+In+Proceedings+of+the+AAAI+conference+on+artificial+in)
8. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, Haofen Wang, and 1 others. 2023. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). arXiv preprint arXiv:2312.10997.
9. Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulatio… [find](https://scholar.google.com/scholar?q=Ary+L+Goldberger%2C+Luis+AN+Amaral%2C+Leon+Glass%2C+Jeffrey+M+Hausdorff%2C+Plamen+Ch+Ivanov%2C+Roger+G+Mark%2C+Joseph+E+Mietus%2C+George+B+Moody%2C+Chung%2DKang+Peng%2C+and+H+Eugene+Stanley%2E+2000%2E+Physiobank%2C+physiotoolk)
10. Miguel A Hernán and James M Robins. 2016. Using big data to emulate a target trial when a randomized trial is not available. American journal of epidemiology, 183(8):758–764. [find](https://scholar.google.com/scholar?q=Miguel+A+Hern%C3%A1n+and+James+M+Robins%2E+2016%2E+Using+big+data+to+emulate+a+target+trial+when+a+randomized+trial+is+not+available%2E+American+journal+of+epidemiology%2C+183%288%29%3A758%E2%80%93764%2E)
11. George Hripcsak, Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech Huser, Martijn J Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei Wong, Peter R Rijnbeek, and 1 others. 2015. Observational health data sciences and informatics (ohdsi): opportunities for observational researchers. Studies in… [find](https://scholar.google.com/scholar?q=George+Hripcsak%2C+Jon+D+Duke%2C+Nigam+H+Shah%2C+Christian+G+Reich%2C+Vojtech+Huser%2C+Martijn+J+Schuemie%2C+Marc+A+Suchard%2C+Rae+Woong+Park%2C+Ian+Chi+Kei+Wong%2C+Peter+R+Rijnbeek%2C+and+1+others%2E+2015%2E+Observational+h)
12. Nicholas R Jennings, Katia Sycara, and Michael Wooldridge. 1998. A roadmap of agent research and development. Autonomous agents and multi-agent systems, 1(1):7–38. [find](https://scholar.google.com/scholar?q=Nicholas+R+Jennings%2C+Katia+Sycara%2C+and+Michael+Wooldridge%2E+1998%2E+A+roadmap+of+agent+research+and+development%2E+Autonomous+agents+and+multi%2Dagent+systems%2C+1%281%29%3A7%E2%80%9338%2E)
13. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33:1877–1901.
14. Wendy W Chapman, Will Bridewell, Paul Hanbury, Gregory F Cooper, and Bruce G Buchanan. 2001. A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of biomedical informatics, 34(5):301–310. [find](https://scholar.google.com/scholar?q=Wendy+W+Chapman%2C+Will+Bridewell%2C+Paul+Hanbury%2C+Gregory+F+Cooper%2C+and+Bruce+G+Buchanan%2E+2001%2E+A+simple+algorithm+for+identifying+negated+findings+and+diseases+in+discharge+summaries%2E+Journal+of+biomedi)
15. Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. [Mimic-iii, a freely accessible critical care database](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/). Scientific data, 3(1):1–9.
16. Yuhe Ke, Rui Yang, Sui An Lie, Taylor Xin Yi Lim, Yilin Ning, Irene Li, Hairil Rizal Abdullah, Daniel Shu Wei Ting, and Nan Liu. 2024. Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: simulation study. Journal of Medical Internet… [find](https://scholar.google.com/scholar?q=Yuhe+Ke%2C+Rui+Yang%2C+Sui+An+Lie%2C+Taylor+Xin+Yi+Lim%2C+Yilin+Ning%2C+Irene+Li%2C+Hairil+Rizal+Abdullah%2C+Daniel+Shu+Wei+Ting%2C+and+Nan+Liu%2E+2024%2E+Mitigating+cognitive+biases+in+clinical+decision%2Dmaking+through+m)
17. Patrick B Ryan, Martijn J Schuemie, Susan Gruber, Ivan Zorych, and David Madigan. 2013. Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system. Drug safety, 36(Suppl 1):59–72. [find](https://scholar.google.com/scholar?q=Patrick+B+Ryan%2C+Martijn+J+Schuemie%2C+Susan+Gruber%2C+Ivan+Zorych%2C+and+David+Madigan%2E+2013%2E+Empirical+performance+of+a+new+user+cohort+method%3A+lessons+for+developing+a+risk+identification+and+analysis+sys)
18. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020. [Biobert: a pre-trained biomedical language representation model for biomedical text mining](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/). Bioinformatics, 36(4):1234–1240.
19. Guergana K Savova, James J Masanz, Philip V Ogren, Jiaping Zheng, Sunghwan Sohn, Karin C Kipper-Schuler, and Christopher G Chute. 2010. Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications. Journal of the American Medical Informati… [find](https://scholar.google.com/scholar?q=Guergana+K+Savova%2C+James+J+Masanz%2C+Philip+V+Ogren%2C+Jiaping+Zheng%2C+Sunghwan+Sohn%2C+Karin+C+Kipper%2DSchuler%2C+and+Christopher+G+Chute%2E+2010%2E+Mayo+clinical+text+analysis+and+knowledge+extraction+system+%28cta)
20. Peter Lee, Sebastien Bubeck, and Joseph Petro. 2023. Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine. New England Journal of Medicine, 388(13):1233–1239. [find](https://scholar.google.com/scholar?q=Peter+Lee%2C+Sebastien+Bubeck%2C+and+Joseph+Petro%2E+2023%2E+Benefits%2C+limits%2C+and+risks+of+gpt%2D4+as+an+ai+chatbot+for+medicine%2E+New+England+Journal+of+Medicine%2C+388%2813%29%3A1233%E2%80%931239%2E)
21. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others. 2020. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Advances in neural information processing systems, 33:9459–…
22. Bertalan Meskó and Eric J Topol. 2023. The imperative for regulatory oversight of large language models (or generative ai) in healthcare. NPJ digital medicine, 6(1):120. [find](https://scholar.google.com/scholar?q=Bertalan+Mesk%C3%B3+and+Eric+J+Topol%2E+2023%2E+The+imperative+for+regulatory+oversight+of+large+language+models+%28or+generative+ai%29+in+healthcare%2E+NPJ+digital+medicine%2C+6%281%29%3A120%2E)
23. Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz. 2023. [Capabilities of gpt-4 on medical challenge problems](https://md.research.elamur.ai/free/en/papers/nori-2023-capabilities-gpt-4-medical-challenge/). arXiv preprint arXiv:2303.13375.
24. OHDSI. 2021. Usagi documentation. Observational Health Data Sciences and Informatics. Available from: [https://www.ohdsi.org/web/wiki/doku](https://www.ohdsi.org/web/wiki/doku). php?id=documentation:software:usagi. [↗](https://www.ohdsi.org/web/wiki/doku)
25. J Marc Overhage, Patrick B Ryan, Christian G Reich, Abraham G Hartzema, and Paul E Stang. 2012. Validation of a common data model for active safety surveillance research. Journal of the American Medical Informatics Association, 19(1):54–60. [find](https://scholar.google.com/scholar?q=J+Marc+Overhage%2C+Patrick+B+Ryan%2C+Christian+G+Reich%2C+Abraham+G+Hartzema%2C+and+Paul+E+Stang%2E+2012%2E+Validation+of+a+common+data+model+for+active+safety+surveillance+research%2E+Journal+of+the+American+Medic)
26. Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. 2020. Monotonic value function factorisation for deep multi-agent reinforcement learning. Journal of Machine Learning Research, 21(178):1–51. [find](https://scholar.google.com/scholar?q=Tabish+Rashid%2C+Mikayel+Samvelyan%2C+Christian+Schroeder+De+Witt%2C+Gregory+Farquhar%2C+Jakob+Foerster%2C+and+Shimon+Whiteson%2E+2020%2E+Monotonic+value+function+factorisation+for+deep+multi%2Dagent+reinforcement+le)
27. Jenna M Reps, Martijn J Schuemie, Marc A Suchard, Patrick B Ryan, and Peter R Rijnbeek. 2018. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. Journal of the American Medical Informatics Association, 2… [find](https://scholar.google.com/scholar?q=Jenna+M+Reps%2C+Martijn+J+Schuemie%2C+Marc+A+Suchard%2C+Patrick+B+Ryan%2C+and+Peter+R+Rijnbeek%2E+2018%2E+Design+and+implementation+of+a+standardized+framework+to+generate+and+evaluate+patient%2Dlevel+prediction+mo)
28. Cynthia Rudin. 2019. [Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/). Nature machine intelligence, 1(5):206–215.
29. Martijn J Schuemie, Patrick B Ryan, Nicole Pratt, Rui-Jun Chen, Seng Chan You, Harlan M Krumholz, David Madigan, George Hripcsak, and Marc A Suchard. 2020. Principles of large-scale evidence generation and evaluation across a network of databases (legend). Journal of the American Medical Informatics… [find](https://scholar.google.com/scholar?q=Martijn+J+Schuemie%2C+Patrick+B+Ryan%2C+Nicole+Pratt%2C+Rui%2DJun+Chen%2C+Seng+Chan+You%2C+Harlan+M+Krumholz%2C+David+Madigan%2C+George+Hripcsak%2C+and+Marc+A+Suchard%2E+2020%2E+Principles+of+large%2Dscale+evidence+generatio)
30. Savyasachi V Shah. 2024. Accuracy, consistency, and hallucination of large language models when analyzing unstructured clinical notes in electronic medical records. JAMA Network Open, 7(8):e2425953. [find](https://scholar.google.com/scholar?q=Savyasachi+V+Shah%2E+2024%2E+Accuracy%2C+consistency%2C+and+hallucination+of+large+language+models+when+analyzing+unstructured+clinical+notes+in+electronic+medical+records%2E+JAMA+Network+Open%2C+7%288%29%3Ae2425953%2E)
31. K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, and 1 others. 2023. [Large language models encode clinical knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/). Nature, 620(7972):172– 180.
32. Ergin Soysal, Jingqi Wang, Min Jiang, Yonghui Wu, Serguei Pakhomov, Hongfang Liu, and Hua Xu. 2018. Clamp–a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association, 25(3):331–336. [find](https://scholar.google.com/scholar?q=Ergin+Soysal%2C+Jingqi+Wang%2C+Min+Jiang%2C+Yonghui+Wu%2C+Serguei+Pakhomov%2C+Hongfang+Liu%2C+and+Hua+Xu%2E+2018%2E+Clamp%E2%80%93a+toolkit+for+efficiently+building+customized+clinical+natural+language+processing+pipelines%2E+)
33. Peter Stone and Manuela Veloso. 2000. Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, 8(3):345–383. [find](https://scholar.google.com/scholar?q=Peter+Stone+and+Manuela+Veloso%2E+2000%2E+Multiagent+systems%3A+A+survey+from+a+machine+learning+perspective%2E+Autonomous+Robots%2C+8%283%29%3A345%E2%80%93383%2E)
34. Marc A Suchard, Martijn J Schuemie, Harlan M Krumholz, Seng Chan You, RuiJun Chen, Nicole Pratt, Christian G Reich, Jon Duke, David Madigan, George Hripcsak, and 1 others. 2019. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinationa… [find](https://scholar.google.com/scholar?q=Marc+A+Suchard%2C+Martijn+J+Schuemie%2C+Harlan+M+Krumholz%2C+Seng+Chan+You%2C+RuiJun+Chen%2C+Nicole+Pratt%2C+Christian+G+Reich%2C+Jon+Duke%2C+David+Madigan%2C+George+Hripcsak%2C+and+1+others%2E+2019%2E+Comprehensive+comparat)
35. Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, and Raul Vicente. 2017. Multiagent cooperation and competition with deep reinforcement learning. PloS one, 12(4):e0172395. [find](https://scholar.google.com/scholar?q=Ardi+Tampuu%2C+Tambet+Matiisen%2C+Dorian+Kodelja%2C+Ilya+Kuzovkin%2C+Kristjan+Korjus%2C+Juhan+Aru%2C+Jaan+Aru%2C+and+Raul+Vicente%2E+2017%2E+Multiagent+cooperation+and+competition+with+deep+reinforcement+learning%2E+PloS)
36. Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023. Large language models in medicine. Nature medicine, 29(8):1930– 1940. [find](https://scholar.google.com/scholar?q=Arun+James+Thirunavukarasu%2C+Darren+Shu+Jeng+Ting%2C+Kabilan+Elangovan%2C+Laura+Gutierrez%2C+Ting+Fang+Tan%2C+and+Daniel+Shu+Wei+Ting%2E+2023%2E+Large+language+models+in+medicine%2E+Nature+medicine%2C+29%288%29%3A1930%E2%80%93+1940)
37. Özlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall. 2011. 2010 i2b2/va challenge on concepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Association, 18(5):552–556. [find](https://scholar.google.com/scholar?q=%C3%96zlem+Uzuner%2C+Brett+R+South%2C+Shuying+Shen%2C+and+Scott+L+DuVall%2E+2011%2E+2010+i2b2%2Fva+challenge+on+concepts%2C+assertions%2C+and+relations+in+clinical+text%2E+Journal+of+the+American+Medical+Informatics+Associa)
38. Erica A Voss, Rupa Makadia, Amy Matcho, Qianli Ma, Chris Knoll, Martijn Schuemie, Frank J DeFalco, Ajit Londhe, Vivienne Zhu, and Patrick B Ryan. 2015. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. Journal of the American Medi… [find](https://scholar.google.com/scholar?q=Erica+A+Voss%2C+Rupa+Makadia%2C+Amy+Matcho%2C+Qianli+Ma%2C+Chris+Knoll%2C+Martijn+Schuemie%2C+Frank+J+DeFalco%2C+Ajit+Londhe%2C+Vivienne+Zhu%2C+and+Patrick+B+Ryan%2E+2015%2E+Feasibility+and+utility+of+applications+of+the+c)
39. Fei Wang, Rainu Kaushal, and Dhruv Khullar. 2020. Should health care demand interpretable artificial intelligence or accept “black box” medicine? [find](https://scholar.google.com/scholar?q=Fei+Wang%2C+Rainu+Kaushal%2C+and+Dhruv+Khullar%2E+2020%2E+Should+health+care+demand+interpretable+artificial+intelligence+or+accept+%E2%80%9Cblack+box%E2%80%9D+medicine%3F)
40. Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, and 1 others. 2018. Clinical information extraction applications: a literature review. Journal of biomedical informatics, 77:34–49. [find](https://scholar.google.com/scholar?q=Yanshan+Wang%2C+Liwei+Wang%2C+Majid+Rastegar%2DMojarad%2C+Sungrim+Moon%2C+Feichen+Shen%2C+Naveed+Afzal%2C+Sijia+Liu%2C+Yuqun+Zeng%2C+Saeed+Mehrabi%2C+Sunghwan+Sohn%2C+and+1+others%2E+2018%2E+Clinical+information+extraction+app)
41. Andrew Wen, Liwei Wang, Huan He, Sunyang Fu, Sijia Liu, David A Hanauer, Daniel R Harris, Ramakanth Kavuluru, Rui Zhang, Karthik Natarajan, and 1 others. 2024. A case demonstration of the open health natural language processing toolkit from the national covid-19 cohort collaborative and the research… [find](https://scholar.google.com/scholar?q=Andrew+Wen%2C+Liwei+Wang%2C+Huan+He%2C+Sunyang+Fu%2C+Sijia+Liu%2C+David+A+Hanauer%2C+Daniel+R+Harris%2C+Ramakanth+Kavuluru%2C+Rui+Zhang%2C+Karthik+Natarajan%2C+and+1+others%2E+2024%2E+A+case+demonstration+of+the+open+health+)
42. Michael Wooldridge. 2009. An introduction to multiagent systems. John wiley & sons. [find](https://scholar.google.com/scholar?q=Michael+Wooldridge%2E+2009%2E+An+introduction+to+multiagent+systems%2E+John+wiley+%26+sons%2E)
43. Ningyu Zhang, Qianghuai Jia, Kangping Yin, Liang Dong, Feng Gao, and Nengwei Hua. 2020. Conceptualized representation learning for chinese biomedical text mining. arXiv preprint arXiv:2008.10813. — [arXiv:2008.10813](https://arxiv.org/abs/2008.10813) [find](https://scholar.google.com/scholar?q=Ningyu+Zhang%2C+Qianghuai+Jia%2C+Kangping+Yin%2C+Liang+Dong%2C+Feng+Gao%2C+and+Nengwei+Hua%2E+2020%2E+Conceptualized+representation+learning+for+chinese+biomedical+text+mining%2E+arXiv+preprint+arXiv%3A2008%2E10813%2E+%E2%80%94+%5Ba)
44. Yuchen Zheng, Zhenggong Han, Yimin Cai, Xubo Duan, Jiangling Sun, Wei Yang, and Haisong Huang. 2022. An imconvnet-based deep learning model for chinese medical named entity recognition. BMC Medical Informatics and Decision Making, 22(1):303. [find](https://scholar.google.com/scholar?q=Yuchen+Zheng%2C+Zhenggong+Han%2C+Yimin+Cai%2C+Xubo+Duan%2C+Jiangling+Sun%2C+Wei+Yang%2C+and+Haisong+Huang%2E+2022%2E+An+imconvnet%2Dbased+deep+learning+model+for+chinese+medical+named+entity+recognition%2E+BMC+Medical+In)

*Updated 1 November 2026*