Using transformer-based models and social media posts for heat stroke detection
Abstract Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84992-y |
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| author | Sumiko Anno Yoshitsugu Kimura Satoru Sugita |
| author_facet | Sumiko Anno Yoshitsugu Kimura Satoru Sugita |
| author_sort | Sumiko Anno |
| collection | DOAJ |
| description | Abstract Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time–space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks. |
| format | Article |
| id | doaj-art-542fa19965694e82b19a490d6d930893 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-542fa19965694e82b19a490d6d9308932025-02-09T12:36:38ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-024-84992-yUsing transformer-based models and social media posts for heat stroke detectionSumiko Anno0Yoshitsugu Kimura1Satoru Sugita2Graduate School of Global Environmental Studies, Sophia UniversityYanagi PearlsChubu Institute for Advanced Studies, Chubu UniversityAbstract Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time–space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.https://doi.org/10.1038/s41598-024-84992-yEvent-based surveillanceBidirectional encoder representations from transformersLanguage understanding with knowledge-based embeddingsTweetsHeat stroke |
| spellingShingle | Sumiko Anno Yoshitsugu Kimura Satoru Sugita Using transformer-based models and social media posts for heat stroke detection Scientific Reports Event-based surveillance Bidirectional encoder representations from transformers Language understanding with knowledge-based embeddings Tweets Heat stroke |
| title | Using transformer-based models and social media posts for heat stroke detection |
| title_full | Using transformer-based models and social media posts for heat stroke detection |
| title_fullStr | Using transformer-based models and social media posts for heat stroke detection |
| title_full_unstemmed | Using transformer-based models and social media posts for heat stroke detection |
| title_short | Using transformer-based models and social media posts for heat stroke detection |
| title_sort | using transformer based models and social media posts for heat stroke detection |
| topic | Event-based surveillance Bidirectional encoder representations from transformers Language understanding with knowledge-based embeddings Tweets Heat stroke |
| url | https://doi.org/10.1038/s41598-024-84992-y |
| work_keys_str_mv | AT sumikoanno usingtransformerbasedmodelsandsocialmediapostsforheatstrokedetection AT yoshitsugukimura usingtransformerbasedmodelsandsocialmediapostsforheatstrokedetection AT satorusugita usingtransformerbasedmodelsandsocialmediapostsforheatstrokedetection |