Emma Montella
ABSTRACT
In recent years, the use of caesarean sections (CS) has grown, leading more women, especially in developed countries, to choose it as a preferential modality, even without clear clinical needs. Although Caesarean sections are effective in reducing maternal and infant mortality, they can cause significant and sometimes permanent complications. The increase in the CS rate, increases hospital stay and therefore hospital costs. Being able to analyze and even predict the length of stay (LOS) for a rapidly growing procedure becomes a valuable information resource for healthcare managers. The purpose of this study is to study LOS for all patients undergoing CS both in the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno and in the A.O.R.N. "Antonio Cardarelli" of Naples. With multiple linear regression analysis and machine learning algorithms we can create a model for LOS prediction.
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- Regression and classification methods for predicting the length of hospital stay after cesarean section: a bicentric study
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