Length of stay prediction and analysis through a growing neural gas model

Length of stay (LoS) prediction is considered an important research field in Healthcare Informatics as it can help to improve hospital bed and resource management. The health cost containment process carried out in Italian local healthcare systems makes this problem particularly challenging in healthcare services management. In this work a novel unsupervised LoS prediction model is presented which performs better than other ones commonly used in this kind of problem. The developed model detects autonomously the subset of non-class attributes to be considered in these classification tasks, and the structure of the trained selforganizing network can be analysed in order to extract the main factors leading to the overcoming of regional LoS threshold.

• Type: Journal
• Year: 2015
• Where: AIME
• Authors: Luigi Lella, Antonio Di Giorgio, Aldo Franco Dragoni
• Keywords: AIME 2015, Artificial Intelligence