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Predictive Factors of Response to Treatment Offered by SUS in Women Crack Cocaine Users: Epigenomic Analyses and Supervised Learning Algorithms

This project will identify clinical, sociodemographic, psychosocial, neurocognitive and epigenomic factors to assist in the identification of the most effective response to the treatment offered by SUS to detoxify the use of crack and cocaine by women. The project will use the Random Forest algorithm in a database developed by the research group itself in order to predict the factors that impact adherence and maintenance of abstinence among users.

More information about Grand Challenges Explorations – Brazil: Data Science Approaches to Improve Maternal and Child Health, Women's Health and Children's Health in Brazil

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View the Grand Challenges partnership network

The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.