ArticleScience advances2023
Predicting food crises using news streams.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 25 citations in OpenAlex.
- Responsible use of artificial intelligence and machine learning for food security early warning systems.Nature food · 2026Article
- Disaster Storylines and Knowledge Graphs from Global News with Large Language Models and Retrieval-Augmented Generation.Scientific data · 2026Article
- Predicting risk of inadequate micronutrient intake with transferable machine learning models.Scientific reports · 2026Article
- Deeply divergent human exposure to food crises across socioeconomic pathways.Scientific reports · 2025Article
- Text mining and machine learning reveal global determinants of food insecurity.Scientific reports · 2025Article
- High-frequency monitoring enables machine learning-based forecasting of acute child malnutrition for early warning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- MSTN knockout enhances the production of MYOD1-mediated steak-type cultivated meat.Journal of animal science and biotechnology · 2025Article
- Large language models can help boost food production, but be mindful of their risks.Frontiers in artificial intelligence · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anticipating food crisis outbreaks is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing predictive models rely on risk measures that are often delayed, outdated, or incomplete. Using the text of 11.2 million news articles focused on food-insecure countries and published between 1980 and 2020, we leverage recent advances in deep learning to extract high-frequency precursors to food crises that are both interpretable and validated by traditional risk indicators. We demonstrate that over the period from July 2009 to July 2020 and across 21 food-insecure countries, news indicators substantially improve the district-level predictions of food insecurity up to 12 months ahead relative to baseline models that do not include text information. These results could have profound implications on how humanitarian aid gets allocated and open previously unexplored avenues for machine learning to improve decision-making in data-scarce environments.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.