Patient length of stay analysis with machine learning algorithms

In this paper the problem of measuring factor importance on patient length of stay in an emergency department is discussed. Historical dataset contains average patient length of stay per day. Factors are agreed with domain expert. The task is to provide factors’ impact measure on specific day that does not belong to the historical dataset (new observation) and average length of stay for that day is higher than specified threshold. Observations are represented as multidimensional numeric vectors. Each dimension represents factor. The basic idea consists of identifying appropriate neighbourhood and measure distances between the new observation and its neighbourhood in the historical dataset with respect to each factor. Impact measure of a factor is derived from the Error Sum of Squares. Factor impact is proportional to distance between the observation and its neighbourhood with respect to the dimension representing that factor. Nearest neighbour and clustering methods for neighbourhood determination are considered.