THE USE OF ARTIFICIAL INTELLIGENCE PREDICTIVE MODELS IN THE PREVENTION OF CHRONIC NON-COMMUNICABLE DISEASES

• Grant number: 24/07524-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2024
• End date: June 30, 2025
• Field of knowledge: Health Sciences - Collective Health - Epidemiology
• Agreement: MCTI/MC
• Principal Investigator: Deborah Carvalho Malta
• Grantee: Crizian Saar Gomes
• Host Institution: Escola de Enfermagem. Universidade Federal de Minas Gerais (UFMG). Ministério da Educação (Brasil). Belo Horizonte , SP, Brazil
• Company: Universidade Federal de Minas Gerais (UFMG). Instituto de Ciências Exatas (ICEx)
• Associated research grant: 20/09866-4 - Artificial Intelligence Innovation Center for Health (CIIA-Health), AP.PCPE

Abstract

Introduction: In the context of prevention and quality of life, the importance of Chronic Non-Communicable Diseases (NCDs) is highlighted, which account for 75% of annual deaths, in addition to high prevalence of risk factors in the Brazilian adult population: 55% overweight, 20% obesity, 10% smoking, which can result in 38.8% of the loss of DALYs (years of life lost due to premature death and disability). Artificial intelligence (AI), defined as the simulation of human intelligence in machines, including learning, reasoning and perception, and related technologies, is a disruptive innovation in the area of health and medicine and offers several ways to improve the quality of life, promote good health conditions for the entire population through preventive strategies, protection against disease, encouragement of healthy lifestyles. AI predictive models have been used to estimate the risk of a given outcome occurring (disease, death), based on the identification of socioeconomic, demographic, and environmental variables, and variables related to lifestyle habits and health conditions, highlighting pioneering experiences such as the Framingham cardiovascular risk score and estimates of health indicators in small geographic areas.Objectives: To develop AI models, techniques, and solutions to identify, analyze, and predict the occurrence of NCDs in population groups considering ethical aspects of privacy and security.Methods: Databases from health information systems, epidemiological surveys and sociodemographic databases and different artificial intelligence techniques for mining, treatment and prediction will be used.Expected results: As a result, it is intended to assign a risk score by strata taking into account variables extracted from the available data. The set of information generated in this project can help managers to better direct interventions and public policies for health promotion customized to the demand and improvements in the health services and care implemented. (AU)