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

  • Dades identificatives

    Identificador: imarina:4768723
    Autors:
    Romero-Aroca, PedroValls-Mateu, AidaMoreno-Ribas, AntonioSagarra-Alamo, RamonBasora-Gallisa, JosepSaleh, EmranBaget-Bernaldiz, MarcPuig, Domenec
    Resum:
    © 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.
  • Altres:

    Autor segons l'article: Romero-Aroca, Pedro; Valls-Mateu, Aida; Moreno-Ribas, Antonio; Sagarra-Alamo, Ramon; Basora-Gallisa, Josep; Saleh, Emran; Baget-Bernaldiz, Marc; Puig, Domenec
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s 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
    Paraules clau: 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
    Resum: © 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 15305627
    Adreça de correu electrònic de l'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 de l'autor: 0000-0003-3945-2314 0000-0002-0562-4205 0000-0002-7061-8987 0000-0003-3616-7809
    Data d'alta del registre: 2024-10-12
    Volum de revista: 25
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.liebertpub.com/doi/10.1089/tmj.2017.0282
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Telemedicine And E-Health. 25 (1): 31-40
    Referència 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
    DOI de l'article: 10.1089/tmj.2017.0282
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2019
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Health Care Sciences & Services,Health Informatics,Health Information Management,Medicine (Miscellaneous)
    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
    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
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