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 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

SUBMITTER: Anno S 

PROVIDER: S-EPMC11698727 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature