Evidential data mining for length of stay (LOS) prediction problem

Hospitals need to optimize their healthcare planning and organization to minimize costs. The indicator that is often used to measure the efficiency in hospital is the average length of stay. Many studies show a strong and obvious correlation between the costs of patients and the impatient Length Of Stay (LOS). In this paper, We propose to apply data mining techniques to predict the LOS. An evidential variant of data mining, called also evidential data mining, have been used to reduce the impact of uncertainty and missing data. New measures of itemset support and association rule confidence are applied. We introduce the Evidential Length Of Stay prediction Algorithm (ELOSA) that allow the prediction of the length of stay of a new patient. Therefore, the inpatient length of stay (LOS) can be predicted efficiently, the planning and management of hospital resources can be greatly enhanced. The proposal is evaluated on a real hospital dataset using 270 patient traces.