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).
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 / G. Damiani, A. Buja, E. Grossi, M. Rivera, A. De Polo, G. De Luca, M. Zorzi, A. Vecchiato, P. Del Fiore, M. Saia, V. Baldo, M. Rugge, C.R. Rossi, G. Damiani. - In: ACTA DERMATO-VENEREOLOGICA. - ISSN 0001-5555. - 100:November(2020 Nov 02).
|Titolo:||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|
DAMIANI, GIOVANNI (Primo)
|Parole Chiave:||costs; cutaneous melanoma; machine learning; non-linear associations; semantic connectivity map; artificial neural networks|
|Settore Scientifico Disciplinare:||Settore MED/35 - Malattie Cutanee e Veneree|
|Data di pubblicazione:||2-nov-2020|
|Data ahead of print / Data di stampa:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.2340/00015555-3680|
|Appare nelle tipologie:||01 - Articolo su periodico|