Artificial neural networks and risk stratification in emergency departments

Abstract

Emergency departments are characterized by the need for quick diagnosis under pressure. To select the most appropriate treatment, a series of rules to support decision-making has been offered by scientific societies. The effectiveness of these rules affects the appropriateness of treatment and the hospitalization of patients. Analyzing a sample of 1844 patients and focusing on the decision to hospitalize a patient after a syncope event to prevent severe short-term outcomes, this work proposes a new algorithm based on neural networks. Artificial neural networks are a non-parametric technique with the well-known ability to generalize behaviors, and they can thus predict severe short-term outcomes with pre-selected levels of sensitivity and specificity. This innovative technique can outperform the traditional models, since it does not require a specific functional form, i.e., the data are not supposed to be distributed following a specific design. Based on our results, the innovative model can predict hospitalization with a sensitivity of 100% and a specificity of 79%, significantly increasing the appropriateness of medical treatment and, as a result, hospital efficiency. According to Garson's Indexes, the most significant variables are exertion, the absence of symptoms, and the patient's gender. On the contrary, cardio-vascular history, hypertension, and age have the lowest impact on the determination of the subject's health status. The main application of this new technology is the adoption of smart solutions (e.g., a mobile app) to customize the stratification of patients admitted to emergency departments (ED)s after a syncope event. Indeed, the adoption of these smart solutions gives the opportunity to customize risk stratification according to the specific clinical case (i.e., the patient's health status) and the physician's decision-making process (i.e., the desired levels of sensitivity and specificity). Moreover, a decision-making process based on these smart solutions might ensure a more effective use of available resources, improving the management of syncope patients and reducing the cost of inappropriate treatment and hospitalization.

Phased genome sequence of an interspecific hybrid flowering cherry, ‘Somei-Yoshino’ (Cerasus × yedoensis)

“…Our transcriptome data for flowering cherry successfully revealed the comprehensive changes in gene expression during floral bud development towards flowering. The expression patterns of above genes in this study and supposed regulation network for dormancy release of woody plants 10 , 73 , 74 are jointly summarized in Supplementary Fig. S10.…”

Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004–2019

“…The artificial neural network (ANN) is a computing system made up of several simple and highly interconnected processing elements, those mimic neurons and process information through their dynamic state responses to external input (13). ANN is equivalent to network operation, that consists of different layers, an input layer, a hidden layer (black box), and an output layer (Figure 1).…”

A Feature Optimized Deep Learning Model for Clinical Data Mining

“…The traditional statistical analysis methods have some shortcomings, such as low efficiency and accuracy, in the face of high-dimensional and large-scale Intensive care database. Machine learning, including semiotic learning represented by decision tree model [2] , connectionist learning represented by Artificial neural network (ANN) model [3,4] , and statistical learning represented by Support vector machine (SVM) model [5] has gradually led the research of AI. AI algorithm can use a large number of sample data for data training and knowledge extraction, analyze the actual application effect of the model, and make corresponding feedback and adjustment to the model.…”