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. 2019 Jan;25(1):31-40.
doi: 10.1089/tmj.2017.0282. Epub 2018 Feb 21.

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

Pedro Romero-Aroca  1 , Aida Valls  2 , Antonio Moreno  2 , Ramon Sagarra-Alamo  3 , Josep Basora-Gallisa  3 , Emran Saleh  2 , Marc Baget-Bernaldiz  1 , Domenec Puig  2
Affiliations

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

Pedro Romero-Aroca et al. Telemed J E Health. 2019 Jan.

Abstract

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.

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Conflict of interest statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the article.

Figures

<b>Fig. 1.</b>
Fig. 1.
Flowchart of the data analysis and CDSS construction and validation process. CDSS, clinical decision support system.
<b>Fig. 2.</b>
Fig. 2.
Screenshot of the developed CDSS, with risk factors at the top and the calculation of diabetic retinopathy risk at the bottom. Color images available online at www.liebertpub.com/tmj
<b>Fig. 3.</b>
Fig. 3.
Example of CDSS in two men, at left with good metabolic control and at right with bad metabolic control. Color images available online at www.liebertpub.com/tmj
<b>Fig. 4.</b>
Fig. 4.
CDSS scheme for a screening program for diabetic retinopathy. Color images available online at www.liebertpub.com/tmj
See this image and copyright information in PMC

References

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