Tracking financing for global common goods for health: A machine learning approach using natural language processing techniques.

Journal: Frontiers in public health
Published Date: Nov 17, 2022

Abstract

OBJECTIVE: Tracking global health funding is a crucial but time consuming and labor-intensive process. This study aimed to develop a framework to automate the tracking of global health spending using natural language processing (NLP) and machine learning (ML) algorithms. We used the global common goods for health (CGH) categories developed by Schäferhoff et al. to design and evaluate ML models.

Authors

  • Siddharth Dixit
    Center for Policy Impact in Global Health, Duke Global Health Institute, Duke University, Durham, NC, United States.
  • Wenhui Mao
    Center for Policy Impact in Global Health, Duke Global Health Institute, Duke University, Durham, NC, United States.
  • Kaci Kennedy McDade
    Center for Policy Impact in Global Health, Duke Global Health Institute, Duke University, Durham, NC, United States.
  • Marco Schäferhoff
    Open Consultants, Berlin, Germany.
  • Osondu Ogbuoji
    Center for Policy Impact in Global Health, Duke Global Health Institute, Duke University, Durham, NC, United States.
  • Gavin Yamey
    Center for Policy Impact in Global Health, Duke Global Health Institute, Duke University, Durham, NC, United States.

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