"THE USE OF BIG DATA AND MACHINE LEARNING IN THE DIAGNOSIS AND TREATMENT OF ATTENTION DEFICIT HYPERACTIVITY DISORDER: A NARRATIVE REVIEW" (Atena Editora)

Abstract

Introduction: Big Data has improved the ability of machines to make intelligent decisions given the high volume, speed and variety of information available, enabling Machine Learning (ML), an artificial intelligence model that uses algorithms that identify and analyze data patterns and make own decisions. In healthcare, ML is recognized for its predictive capacity and for increasing the accuracy of decisions; and has already been used in studies, diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity and impulsivity. Purpose: To document the importance of Big Data and artificial intelligence in scientific predictions about ADHD. Methodology: The narrative review used PubMed and SCOPUS databases and the descriptors "ADHD", "Attention Deficit Hyperactivity Disorder" and "Big Data". Articles that used Big Data to obtain data analyzed by algorithms on ADHD were included. Articles related to case studies, literature review, animal model and those not available in English were excluded. Results and discussion: 17 studies were reviewed. The use of Big Data has changed the understanding of the pathogenesis, comorbidities, diagnosis and treatment of various diseases. Tools, such as the Spatial Transformation Model STM), which allow the recording and analysis of neuroimaging patterns in identified ADHD patients, help to determine neuroanatomical characteristics in the disease phenotype. It was also possible to analyze ADHD susceptibility genes, such as the DAT1 dopamine transporter gene, and verify the predictive value of the ML used to determine the relationship between behavioral patterns in social networks and the diagnosis of ADHD. With regard to treatment, Big Data has enabled greater knowledge about the impacts of the use of antipsychotics, the subject of great controversy. Conclusion: Big Data applied to research on ADHD acts as a new tool for the assessment and treatment of this disorder, which increases the quality of research on the subject, enabling the improvement of neuropsychiatry services, facilitating the screening and management of this disorder and reduces the economic burden of disease.