State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning

Abstract

Length of stay (LOS) is a key indicator of healthcare quality and reflects the burden on the healthcare system. However, limited studies have used machine learning to predict LOS in asthma. This study aimed to explore the characteristics and associations between asthma-related admission data variables with LOS and to use those factors to predict LOS. A dataset of asthma-related admissions in the Auckland region was analysed using different statistical techniques. Using those predictors, machine learning models were built to predict LOS. Demographic, diagnostic, and temporal factors were associated with LOS. Māori females had the highest average LOS among all the admissions at 2.8 days. The random forest algorithm performed well, with an RMSE of 2.48, MAE of 1.67, and MSE of 6.15. The mean predicted LOS by random forest was 2.6 days with a standard deviation of 1.0. The other three algorithms were also acceptable in predicting LOS. Implementing more robust machine learning classifiers, such as artificial neural networks, could outperform the models used in this study. Future work to further develop these models with other regions and to identify the reasons behind the shorter and longer stays for asthma patients is warranted.