Abstract: The primary
goal of the Emergency Department physician is to discriminate individuals at
low risk, who can be safely discharged, from patients at high risk, who deserve
prompt hospitalization for monitoring and/or appropriate treatment. Obviously,
the problem of a correct classification of patients, and the successive
hospital admission, is not only a clinical issue but also a management one
since ameliorating the rate of admission of patients in the emergency
departments could dramatically reduce costs and create a better health resource
use.
Considering
patients at the emergency departments after an event of syncope, this work
propose a comparative analysis between multivariate logistic regression model
and Artificial Neural Networks (ANNs), highlighting the difference in correct
classification of severe outcome at 10 days and 1 year. According to results,
ANNs can be very effective in classifying the risk of severe outcomes and it
might be adopted to support the physician decision making process reducing, at
least theoretically, the inappropriate admission of patients after syncope
event.
keywords: Artificial
Neural Networks (ANNs); Syncope; Emergency Departments; Risk stratification;
Area Under the Curve, referring to the Receiver Operating Characteristics (ROC)
Curve; correct classification;
JEL Codes: I12; D81;
The preliminary results of this paper
have been presented at the workshop “Progettare
per innovare: le reti” (Azienda Ospedaliera “SS. Antonio e Biagio e Cesare Arrigo” of Alessandria –
18th July, 2014).