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

作者：Lea Berrang‐Ford, Anne J. Sietsma, Max Callaghan, Jan Christoph Minx, Pauline Scheelbeek, Neal Haddaway, Andy Haines, Alan D. Dangour · 发表于：The Lancet Planetary Health · 年份：2021 · DOI：10.1016/s2542-5196(21)00179-0 · 被引用次数：231 · 研究领域：Climate Change and Health Impacts、Climate Change Communication and Perception、Climate change impacts on agriculture

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 searched for papers including both a health component and an explicit mention of either climate change, climate variability, or climate change-relevant weather phenomena. We classified relevant publications according to the fields of climate research, climate drivers, health impact, date, and geography. We used supervised and unsupervised machine learning to identify and classify relevant articles in the field of climate and health, with outputs including evidence heat maps, geographical maps, and narrative synthesis of trends in climate health-related publications. We included empirical literature of any study design that reported on health pathways associated with climate impacts, mitigation, or adaptation. ...