A multiplicative Holt–Winters model and autoregressive moving-average for hyponatremia mortality rates

Abstract

This study presents the effective utilization of data analysis in efficiently managing medical resources — a crucial factor in ensuring high-quality healthcare delivery and positive patient outcomes. We employ a multiplicative Holt-Winters model and a linear model with an autoregressive moving-average time series error term and analyze time series mortality rates associated with hyponatremia, a condition that leads to a systemic electrolyte imbalance. The time series data consistently reveals noticeable trends and recurring seasonal patterns. To address these intricate patterns, the proposed data analysis approaches adeptly incorporate these features, thereby yielding relatively accurate point forecasts and reliable forecast intervals. Beyond mere forecasting, the autoregressive moving-average model approach offers insights into the overarching trend and facilitates the comparison of seasonal components. One of the approaches is well-suited for the specific time series related to hyponatremia and holds the potential for extension to incorporate more complex trends beyond linearity. The research demonstrates the synergistic relationship between data analysis and medical expertise, working in tandem to achieve the optimal allocation of medical resources. We strongly advocate for the routine integration of statistical analyses as an essential practice to foster this harmonious synergy.

Keywords

• Hyponatremia mortality rate
• Trend
• Seasonal component
• Multiplicative Holt–Winters models
• Autoregressive moving-average models
• Time series forecast intervals