Predicting hospital length of stay in hepatitis B patients: a comparison of survival regression models
DOI:
https://doi.org/10.64497/jssci.187Keywords:
survival analysis, length of stay, Weibull model, Kaplan-Meier estimation, hepatitis-bAbstract
Hospital length of stay (LOS) is an important indicator of healthcare efficacy and patient outcomes in the treatment of chronic infectious diseases such as hepatitis B virus (HBV) infection. Accurate LOS modeling can improve patient care and optimize resource allocation for hospital management and clinicians. This study employs survival analysis to predict length of stay (LOS) among hospitalized hepatitis B patients using Kaplan-Meier estimation, exponential, Weibull, and log-normal distributions. The non-parametric Kaplan-Meier estimator was used to examine the empirical survival pattern before parametric models were fitted. Model parameters were estimated using maximum likelihood estimation, and right censoring was assumed while evaluating time-to-discharge data. Model adequacy and comparative performance were assessed using log-likelihood values, the Bayesian Information Criterion (BIC), and the Akaike Information Criterion (AIC). The Weibull model offers the greatest fit to the LOS data, according to the results, indicating the existence of a non-constant hazard rate over time. The results show that flexible parametric survival models are appropriate for hospital LOS analysis and offer a statistical foundation for enhancing discharge planning and health system effectiveness in hospitalizations associated with HBV.
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