Open AccessA Clinical Decision Support System to Stratify the Temporal Risk of Diabetic Retinopathy
Author(s) -
Michele Bernardini,
Luca Romeo,
Adriano Mancini,
Emanuele Frontoni
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3127274
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Diabetic Retinopathy (DR) is the most common and insidious microvascular complication of diabetes, and can progress asymptomatically until a sudden loss of vision occurs. Although DR is prevalent nowadays, its prevention remains challenging. The multiple aim of this study was to predict the risk of developing DR as diabetic complication (task 1) and, subsequently, temporally stratify the DR risk (task 2) using electronic health records data. To perform these objectives, a novel preprocessing procedure was designed to select both control and pathological patients, and moreover, a novel fully annotated/standardized 120K dataset from multiple diabetologic centers was provided. Globally, although the Extreme Gradient Boosting model offers satisfying predictive performance, the Random Forest model obtained the best predictive performance to solve task 1 and task 2, reaching the best Area Under the Precision-Recall Curve of 72.43 % and 84.38 %, respectively. Also the features importance extracted from the best Machine Learning (ML) models is provided. The proposed Artificial Intelligence-based solution was proven to be capable of generalizing across different diabetologic centers while ensuring high-interpretability. Moreover, the proposed ML solution is currently being adopted as a Clinical Decision Support System in several diabetologic centers for DR screening and follow-up purposes.