Forecasting daily bathtub-drowning mortality in Japan: a comparative analysis of statistical, machine learning, and deep learning approaches
The DLNM-based framework provides a practical means of forecasting the daily bathtub-drowning deaths. Integration into routine meteorological broadcasts and mobile platforms may facilitate timely warnings, prompting older adults to avoid unsupervised bathing on high-risk days, thereby reducing Japan's ongoing preventable bath-related mortality.
Authors
- Kenji Obayashi (ORCID: https://orcid.org/0000-0001-6346-9082)
- Yoshiaki Tai (ORCID: https://orcid.org/0000-0001-6204-113X)
- Yuki Yamagami (ORCID: https://orcid.org/0000-0002-8415-6461)
- Keigo Saeki (ORCID: https://orcid.org/0000-0001-6465-8205)
Institutions
- Nara Medical University (JP)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-12-10
- DOI
- https://doi.org/10.3389/fpubh.2025.1715622
- Citations
- 1
- Primary Topic
- Injury Epidemiology and Prevention
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Japan Society for the Promotion of Science