Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloArtificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective
Anno di pubblicazione2016
  • Elettronico
  • Cartaceo
Autore/iCasagranda I., Costantino G., Falavigna G., Furlan R., Ippoliti R.,
Affiliazioni autoriaEmergency Department, "SS Antonio e Biagio e Cesare Arrigo" Hospital, Alessandria, Italy Internal Medicine Department, "Fondazione IRCCS Ca' Granda" Hospital, Milan, Italy CNR-IRCrES (National Research Council of Italy - Research Institute on Sustainable Economic Growth), Moncalieri (Turin), Italy Scientific Promotion, "SS Antonio e Biagio e Cesare Arrigo" Hospital, Alessandria, Italy and Department of Management, University of Torino, Italy Division of Internal Medicine, Humanitas Research Hospital, Rozzano, Italy and Università degli Studi di Milano, Milan, Italy
Autori CNR e affiliazioni
  • inglese
AbstracttThe primary goal of Emergency Department (ED) physicians is to discriminate betweenindividuals at low risk, who can be safely discharged, and patients at high risk, who requireprompt hospitalization. The problem of correctly classifying patients is an issue involvingnot only clinical but also managerial aspects, since reducing the rate of admission of patientsto EDs could dramatically cut costs. Nevertheless, a trade-off might arise due to the needto find a balance between economic interests and the health conditions of patients.This work considers patients in EDs after a syncope event and presents a comparativeanalysis between two models: a multivariate logistic regression model, as proposed by thescientific community to stratify the expected risk of severe outcomes in the short and longrun, and Artificial Neural Networks (ANNs), an innovative model.The analysis highlights differences in correct classification of severe outcomes at 10 days(98.30% vs. 94.07%) and 1 year (97.67% vs. 96.40%), pointing to the superiority of NeuralNetworks. According to the results, there is also a significant superiority of ANNs in termsof false negatives both at 10 days (3.70% vs. 5.93%) and at 1 year (2.33% vs. 10.07%). However,considering the false positives, the adoption of ANNs would cause an increase in hospitalcosts, highlighting the potential trade-off which policy makers might face.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da111
Pagine a119
Pagine totali-
RivistaHealth policy (Amst. Print)
Attiva dal 1984
Editore: Elsevier - Amsterdam
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0168-8510
Titolo chiave: Health policy (Amst. Print)
Titolo proprio: Health policy. (Amst. Print)
Titolo abbreviato: Health policy (Amst. Print)
Numero volume della rivista120
Fascicolo della rivista-
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveEmergency Department (ED), Risk stratification, Artificial Neural Networks (ANNs), Hospital admission, Syncope
Link (URL, URI)-
Titolo parallelo-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IRCRES — Istituto di Ricerca sulla Crescita Economica Sostenibile
Moduli/Attività/Sottoprogetti CNR
  • IC.P05.015.001 : Competitività di settori e filiere industriali
Progetti Europei-
Artificial Neural Networks and risk stratification models inEmergency Departments: The policy maker's perspective (documento privato )
Tipo documento: application/pdf