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Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models
Rawiyah Muneer Alraddadi1, Mohamed Abd Allah El-Hadidy2, Qin Shao3
1Department of Mathematics and Statistics, College of Science in Yanbu, Taibah University, Madinah, Saudi Arabia.
The International Journal of Biostatistics
|September 2, 2025
Summary
This study forecasts hyponatremia mortality using time series analysis. Predictive analytics can improve patient care and resource allocation for this common electrolyte imbalance.
Area of Science:
- Clinical Medicine
- Biostatistics
- Healthcare Analytics
Background:
- Hyponatremia (serum sodium < 135 mEq/L) is a common electrolyte imbalance.
- It is linked to increased patient morbidity and mortality in various conditions.
- Effective prediction of hyponatremia-related deaths is crucial for healthcare management.
Purpose of the Study:
- To forecast mortality rates associated with hyponatremia.
- To identify temporal patterns and trends in hyponatremia-related deaths.
- To highlight the value of statistical forecasting in healthcare.
Main Methods:
- Utilized the Holt-Winters seasonal method for time series forecasting.
- Analyzed retrospective mortality data from US hospitals.
- Focused on hyponatremia-related mortality trends.
Main Results:
- The study successfully applied time series forecasting to predict hyponatremia mortality.
- Temporal patterns in hyponatremia-related deaths were elucidated.
- Demonstrated the utility of predictive analytics in healthcare.
Conclusions:
- Statistical forecasting is vital for proactive healthcare resource allocation.
- Targeted interventions can mitigate mortality risks from electrolyte imbalances.
- Integrating predictive analytics enhances patient care for hyponatremia complications.
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