Common Data Elements and Features of a Recommender System for People Living with Fatty Liver Disease

Fatty liver disease is rising as the most common liver disease in recent years. One of the new approaches to manage the disease is the use of intelligent systems. The recommender system is increasingly utilized in managing chronic conditions. This study was performed to identify the common data elements and features of a recommender system for people living with fatty liver. This study was a narrative review exploring the minimum data set for a recommender system in fatty liver disease. We aimed to review the current literature evidence to comprehend the specific requirements of the related knowledge. The search was carried out in November 2020 using PubMed, Scopus, Science Direct, and Web of Science databases. We searched the keywords including fatty liver, liver disease, nonalcoholic fatty liver disease, intelligent, smart system, recommender system, minimum data set, data element, and data requirements. A review of the articles showed that the most common data elements of the administrative category were sex/gender (n=22), age (n=22), and ethnic group/race (n=8). We also identified the clinical data elements and technical features of a recommender system for people living with fatty liver. Based on the findings of this study, “diabetes and glucose status” (n=18), “AST” (n=15), “BMI” (n=13), and “ALT” (n=13) were the most frequent data elements of clinical category. Furthermore, “predicting and identifying” (n=8) was the most common technical feature mentioned in the reviewed articles.‎ We determined the common elements and features of a recommender system in ‎three different categories: clinical data elements, demographic data elements, and technical ‎capabilities. Using these requirements, it is possible to structure data gathering, medication ‎adherence, and communication with healthcare providers in a standard manner. It is ‎suggested that appropriate policies and national grants be adopted to identify and prioritize a ‎minimum data set to support the healthcare services of people living with chronic conditions.