Short-Term Lag Effects of Climate-Pollution Interactions on Cardiopulmonary Hospitalizations: A Multi-City Predictive Study Using the AE+LSTM Hybrid Model in Japan.
Our findings reveal the dynamic, time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions. This framework can inform early warning systems, enhance healthcare resource allocation, and support climate-adaptive public health strategies.
Authors
- Yukitaka Ohashi (ORCID: https://orcid.org/0000-0003-0257-828X)
- Tomohiko Ihara (ORCID: https://orcid.org/0000-0002-9252-0091)
- Yi‐Chun Chen (ORCID: https://orcid.org/0000-0002-5205-4006)
- Fan Zhao (ORCID: https://orcid.org/0000-0002-4826-2769)
- Qian Wu
Institutions
- The University of Tokyo (JP)
Publication Details
- Journal
- PubMed
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3967/bes2025.137
- Primary Topic
- Climate Change and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 0.00