ArticleTelemedicine journal and e-health : the official journal of the American Telemedicine Association2019
A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application.
Article in Telemedicine journal and e-health : the official journal of the American Telemedicine Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Designing Clinical Decision Support Systems (CDSS)-A User-Centered Lens of the Design Characteristics, Challenges, and Implications: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Risk prediction models for diabetic retinopathy: a systematic review.Frontiers in endocrinology · 2025Pooled it
- Patient and Physician Perspectives on Oral Treatment in Ophthalmology.Ophthalmology and therapy · 2026Article
- BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.Scientific reports · 2026Article
- Artificial Intelligence-Based Screening System for Diabetic Retinopathy in Primary Care.Diagnostics (Basel, Switzerland) · 2024Article
- Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up.Diagnostics (Basel, Switzerland) · 2024Article
- Clinical decision support systems for diabetic foot ulcers: a scoping review.Revista da Escola de Enfermagem da U S P · 2024Article
- An intelligent decision support system for acute postoperative endophthalmitis: design, development and evaluation of a smartphone application.BMC medical informatics and decision making · 2023Article
- Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population.Clinical ophthalmology (Auckland, N.Z.) · 2022Article
- Validation of a Diagnostic Support System for Diabetic Retinopathy Based on Clinical Parameters.Translational vision science & technology · 2021Article
- [Improved patient safety through a clinical decision support system in laboratory medicine].Der Internist · 2020Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodWe 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).
resultsA 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.
conclusionsWe have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.