Artificial Neural Networks and risk stratification in Emergency department

Ivo Casagranda Ivo, Giorgio Costantino, Greta Falavigna, Raffaello Furlan and Roberto Ippoliti
Additional contact information
Ivo Casagranda Ivo: A.O. “SS. Antonio e Biagio e Cesare Arrigo” di Alessandria, S.C. di Medicina d’urgenza
Giorgio Costantino: A.O. “Luigi Sacco” di Milano, S.C. Medicina a Indirizzo Fisiopatologico
Raffaello Furlan: Istituto Clinico Humanitas, S.C. Clinica Medica

CERIS Working Paper from CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY

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.

JEL-codes: D81 I12 (search for similar items in EconPapers)
Pages: 20 pages Keywords : Artificial Neural Networks, Syncope, Emergency Departments, Risk stratification, Area Under the Curve, referring to the Receiver Operating Characteristics Curve, correct classification
Date: 2014-09
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.byterfly.eu/islandora/object/librib:339694 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:csc:cerisp:201412

Access Statistics for this paper

More papers in CERIS Working Paper from CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY Contact information at EDIRC.
Bibliographic data for series maintained by Anna Perin () and Giancarlo Birello ().