Use of an artificial neural network to identify patient clusters in a large cohort of patients with melanoma by simultaneous analysis of costs and clinical characteristics

Gianfranco Damiani, Giovanni Damiani, Alessandra Buja, Enzo Grossi, Michele Rivera, Anna De Polo, Giuseppe De Luca, Manuel Zorzi, Antonella Vecchiato, Paolo Del Fiore, Mario Saia, Vincenzo Baldo, Massimo Rugge, Carlo Riccardo Rossi

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

The incidence of cutaneous malignant melanoma (CMM) in Italy has increased in the last decade, leading to publichealth concern and rising costs of healthcare (1, 2). In addition to individual susceptibility to development of CMM, several environmental variables influence prognosis in this disease. These variables include social disparities, socioeconomic status, education and marital status (3). How ever, the impact of these variables on costs is unknown. The current study used a new methodology, based on an artificial neural network (ANN), to decodify this complexity by simultaneously describing the relation-ships between clinical, sociodemographic, outcome, and cost variables, and grouping patients into clusters (4, 5).
Lingua originaleEnglish
pagine (da-a)1-3
Numero di pagine3
RivistaActa Dermato-Venereologica
Volume100
DOI
Stato di pubblicazionePubblicato - 2020

Keywords

  • artificial neural networks
  • costs
  • cutaneous melanoma
  • machine learning
  • non-linear associations
  • semantic connectivity map

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