Systematic mapping of global research on climate and health: a machine learning review.

ABSTRACT:

Background

The global literature on the links between climate change and human health is large, increasing exponentially, and it is no longer feasible to collate and synthesise using traditional systematic evidence mapping approaches. We aimed to use machine learning methods to systematically synthesise an evidence base on climate change and human health.

Methods

We used supervised machine learning and other natural language processing methods (topic modelling and geoparsing) to systematically identify and map the scientific literature on climate change and health published between Jan 1, 2013, and April 9, 2020. Only literature indexed in English were included. We searched Web of Science Core Collection, Scopus, and PubMed using title, abstract, and keywords only. We search

SUBMITTER: Berrang-Ford L 

PROVIDER: S-EPMC8358978 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature