Using artificial intelligence to analyze violence against women in São Paulo
ABSTRACT OBJECTIVE To propose the Health Research Standard Process for Artificial Intelligence framework, adapted from the Cross-Industry Standard Process for Data Mining approach, and apply it using time series and artificial intelligence techniques to analyze data from the Sistema de Informação de Agravos de Notificação (SINAN – Notifiable Diseases Information System) on violence against women in São Paulo, exploring sociodemographic, spatial, and predictive dimensions. METHODS The methodology was adapted to health research, replacing the original Business Understanding stage with Research Understanding by incorporating essential technical-scientific elements. We analyzed 80,148 reports of violence against women aged between 20 and 59 living in the city of São Paulo between 2013 and 2023. The analyses included descriptive statistics, calculation of prevalence ratios, decomposition and predictive modeling of time series capturing trends and seasonality, and geospatial analysis of notifications. RESULTS We observed temporal patterns and sociodemographic characteristics of the victims, with increasing trends in reports of physical, psychological, and sexual violence after 2015 and seasonal behavior. Among the associations, there was a 32% increase in the prevalence of sexual violence when the aggressor was under the influence of alcohol and a 2.47 times greater risk of sexual violence among pregnant women. The geospatial distribution revealed concentrations of notifications in peripheral areas of the municipality, such as the south, east, and central zones. Predictive modeling indicated that the upward trend will persist over the next 24 months, with estimated rates reaching up to 12 cases per 100,000 inhabitants. CONCLUSION The applicability of the Health Research Standard Process for Artificial Intelligence was effective as a model for data analysis and the development of predictive algorithms in public health. It was possible to propose a replicable methodological matrix for future research and evidence-based interventions.