Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models
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Rawiyah Muneer Alraddadi
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Qin Shao
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.
Acknowledgments
The author is very grateful to the Editor in Chief, Associate Editor and the anonymous referees for their careful reading of the paper, and for suggestions which helped the author to improve it.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None of these tools were used. Nothing to declare.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: Nothing to declare.
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Data availability: The raw data can be obtained on request from the corresponding author or from: https://hcup-us.ahrq.gov/nisoverview.jsp.
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