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Mapping global research on climate and health using machine learning (a systematic evidence map).

Lea Berrang-Ford1, Anne J Sietsma1, Max Callaghan1,2

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Summary

Machine learning will systematically synthesize global evidence on climate change, climate variability, and weather (CCVW) impacts on human health. This approach addresses the growing literature, offering a comprehensive overview of CCVW and health trends.

Keywords:
Climateadaptationglobalhealthmachine learningmitigationsystematictopic modelling

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Area of Science:

  • Environmental Health
  • Public Health
  • Computational Science

Background:

  • Climate change significantly impacts global public health, threatening recent advancements.
  • Health is affected by climate change and its associated emissions and co-pollutants.
  • Existing literature on climate-health is vast and fragmented, hindering comprehensive synthesis.

Purpose of the Study:

  • To outline a protocol for systematically synthesizing global evidence on the climate change, climate variability, and weather (CCVW) and human health relationship.
  • To employ machine learning for efficient and comprehensive evidence synthesis.
  • To identify trends and map key topics in climate-health research.

Main Methods:

  • Utilizing supervised machine learning to screen over 300,000 scientific articles using CCVW and human health terms.
  • Applying inclusion criteria for articles published 2013-2020 focusing on empirical assessments of CCVW impacts, mitigation, or adaptation.
  • Employing supervised machine learning (topic modeling) for categorization and geographical data extraction.
  • Using unsupervised machine learning (topic modeling) to identify and map key topics and trends.

Main Results:

  • The protocol enables a comprehensive, semi-automated systematic evidence synthesis.
  • Identification and mapping of key topics and trends in climate-health literature.
  • Outputs include evidence heat maps, geographic maps, and narrative synthesis.

Conclusions:

  • This study presents the first comprehensive, semi-automated systematic evidence synthesis of the climate-health literature.
  • Machine learning approaches are crucial for managing and synthesizing the exponentially growing body of climate-health research.
  • The findings will provide a vital overview of global climate-health interactions, impacts, mitigation, and adaptation strategies.