Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature
Lucija Gosak, Kristina Martinović, Mateja Lorber, Gregor Štiglic
Abstract
AIM: The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications. BACKGROUND: In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications. EVALUATION: International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison. KEY ISSUES: Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network. CONCLUSION: The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care. IMPLICATIONS FOR NURSING MANAGEMENT: Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.