Forecasting Return Visits of the Emergency Department

Abstract

Emergency Department (ED) revisits are aggravating ED patient overcrowding. |- ADAPT The use of arti cial intelligence techniques to nd out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction. Methods: In order to support ED manager, we propose the NearMiss algorithm in combination with an ANN approach to forecast ED revisits. { ADAPT We use the Random Forest (RF) technique in order to search for the most important attributes (SNPs) related to diabetes, giving a weight (degree of importance), ranging between 0 and 1, to each attribute. Support vector machines and logistic regression have also been used since they are two other machine learning techniques that are well-established in the health community. Their performance has been compared to that achieved by RF. Furthermore, the relevance of the attributes obtained through the use of RF has then been used to perform predictions with k nearest neighbour method weighting attributes in the similarity measure according to the relevance of the attributes with RF. Results: The method is applied to a dataset of 12 years and outperforms previous works in the literature with a prediction AUC of 89.6%. { ADAPT Testing is performed on a set of 677 subjects. RF is able to handle the complexity of features' interactions, over tting, and unknown attribute values, providing the SNPs' relevance with an up to 0.89 area under the ROC curve in terms of risk prediction. RF outperforms all the other tested machine learning techniques in terms of prediction accuracy, and in terms of the stability of the estimated relevance of the attributes. Conclusions: TODO - ADAPT The random forest is a useful method for learning predictive models and the relevance of SNPs without any underlying assumption