Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade.

Journal: International journal of health geographics
PMID: 40296001

Abstract

BACKGROUND: Illicit kidney trade networks, operating globally, involve intricate interactions among various players, most notably buyers, sellers, brokers, and surgeons. A comprehensive understanding of these trade networks is, however, hindered by the lack of systematically amassed data for analysis. Further, extracting the geographic locations of buyers, sellers, brokers, transplant surgeons, and medical facilities in all relevant publications often involves extensive, time-consuming, manual labelling that is very costly. Although current techniques such as Named Entity Recognition (NER) tools can potentially automate the process, they are limited to identifying country names and often fail to associate the roles (i.e., offering buyer, seller, broker and/or surgery) that each country played.

Authors

  • Zifu Wang
    Department of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, USA. zwang31@gmu.edu.
  • Meng-Hao Li
    Schar School of Policy and Government, George Mason University, Fairfax, USA.
  • Patrick Baxter
    Schar School of Policy and Government, George Mason University, Fairfax, USA.
  • Olzhas Zhorayev
    Schar School of Policy and Government, George Mason University, Fairfax, USA.
  • Jiaxin Wei
    Schar School of Policy and Government, George Mason University, Fairfax, USA.
  • Valerie Kovacs
    Thomas Jefferson High School for Science and Technology, Alexandria, USA.
  • Qiuhan Zhao
    Department of Technology Management for Innovation, The University of Tokyo, Bunkyō, Japan.
  • Chaowei Yang
    Department of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, USA.
  • Naoru Koizumi
    Schar School of Policy and Government, George Mason University, Fairfax, USA.

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