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A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application - imarina:4768723

Autor/es de la URV:ALI, EMRAN SALEH ALI / Baget Bernaldiz, Marc / Basora Gallisa, Josep / Moreno Ribas, Antonio / Puig Valls, Domènec Savi / Romero Aroca, Pedro / Valls Mateu, Aïda
Autor según el artículo:Romero-Aroca, Pedro; Valls-Mateu, Aida; Moreno-Ribas, Antonio; Sagarra-Alamo, Ramon; Basora-Gallisa, Josep; Saleh, Emran; Baget-Bernaldiz, Marc; Puig, Domenec
Direcció de correo del autor:marc.baget@urv.cat
josep.basora@urv.cat
antonio.moreno@urv.cat
domenec.puig@urv.cat
josep.basora@urv.cat
pedro.romero@urv.cat
aida.valls@urv.cat
Identificador del autor:0000-0003-3945-2314
0000-0002-0562-4205
0000-0002-7061-8987
0000-0003-3616-7809
Año de publicación de la revista:2019
Tipo de publicación:Journal Publications
ISSN:15305627
Referencia de l'ítem segons les normes APA:Romero-Aroca, Pedro; Valls-Mateu, Aida; Moreno-Ribas, Antonio; Sagarra-Alamo, Ramon; Basora-Gallisa, Josep; Saleh, Emran; Baget-Bernaldiz, Marc; Puig, (2019). A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application. Telemedicine And E-Health, 25(1), 31-40. DOI: 10.1089/tmj.2017.0282
Referencia al articulo segun fuente origial:Telemedicine And E-Health. 25 (1): 31-40
Resumen:© 2019 Romero-Aroca et al. Background: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients. Method: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR). Results: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria. Discussion: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS. Conclusions: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.
DOI del artículo:10.1089/tmj.2017.0282
Enlace a la fuente original:https://www.liebertpub.com/doi/10.1089/tmj.2017.0282
Versión del articulo depositado:info:eu-repo/semantics/publishedVersion
Acceso a la licencia de uso:https://creativecommons.org/licenses/by/3.0/es/
Departamento:Enginyeria Informàtica i Matemàtiques
URL Documento de licencia:https://repositori.urv.cat/ca/proteccio-de-dades/
Áreas temáticas:Saúde coletiva
Odontología
Medicine (miscellaneous)
Medicina iii
Medicina ii
Medicina i
Interdisciplinar
Health information management
Health informatics
Health care sciences & services
General medicine
Engenharias iv
Engenharias ii
Enfermagem
Educação física
Ciência da computação
Biotecnología
Astronomia / física
Arquitetura, urbanismo e design
Palabras clave:Type-1
Retinopatía diabética
Random forest test
Prevalence
Model
Mellitus
Medical decision making
Fuzzy rules
Follow-up
Epidemiology
E-health
Diabetic retinopathy
Diabetic macular edema
Decision support systems
Cost
Clinical decision support system
Entidad:Universitat Rovira i Virgili
Fecha de alta del registro:2024-10-12
Volumen de revista:25
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