Author
Listed:
- Alraddadi Rawiyah Muneer
(Department of Mathematics and Statistics, College of Science in Yanbu, Taibah University, Madinah, Saudi Arabia)
- El-Hadidy Mohamed Abd Allah
(Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt)
- Shao Qin
(Department of Mathematics and Statistics, The University of Toledo, Toledo, OH, USA)
- Xianggui Qu
(Department of Mathematics and Statistics, Oakland University, Rochester, USA)
- Khuder Sadik
(Department of Medicine, Medical College of Ohio, Toledo, OH 43614-5809, USA)
Abstract
Hyponatremia, characterized by a serum sodium concentration below 135 mEq/L, is a prevalent electrolyte imbalance associated with increased morbidity and mortality across various clinical conditions. This study employs the Holt-Winters seasonal method, a robust time series forecasting model, to predict mortality rates attributed to hyponatremia. Leveraging retrospective mortality data from a cohort of hospitals in the United States, our analysis aims to elucidate temporal patterns and trends in hyponatremia-related deaths. The findings underscore the critical role of statistical forecasting in healthcare, facilitating proactive resource allocation and targeted interventions to mitigate mortality risks associated with electrolyte imbalances. Integrating predictive analytics into clinical practice holds promise for enhancing patient care and optimizing health outcomes in populations vulnerable to hyponatremia-related complications.
Suggested Citation
Alraddadi Rawiyah Muneer & El-Hadidy Mohamed Abd Allah & Shao Qin & Xianggui Qu & Khuder Sadik, 2025.
"Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models,"
The International Journal of Biostatistics, De Gruyter, vol. 21(2), pages 463-471.
Handle:
RePEc:bpj:ijbist:v:21:y:2025:i:2:p:463-471:n:1005
DOI: 10.1515/ijb-2024-0075
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