Project Summary/Abstract Mental health disorders, including anxiety and depression, are common in pediatric patients and significantly impair behavioral function and quality of life. For those with severe illness, patients may be hospitalized for more targeted treatment. Despite medication and/or therapy treatment, children and adolescents are frequently readmitted into psychiatric care as a result of numerous reasons, including treatment ineffectiveness, medication side effects, and issues with adhering to the treatment plan for the disorder. In fact, 25% of youth are readmitted within one year of discharge. Additionally, treatment for these disorders can be long and costly to patients and their families, especially if patients are hospitalized or re-hospitalized, with patients enduring multiple medication trials before finding the best medication. In order to address these issues with pediatric psychiatric readmission, this research is focused on the development of a machine learning algorithm to predict psychiatric readmission in children and adolescents. The first aim of the proposed research is to develop and establish machine learning algorithms to predict psychiatric readmission within 30-, 90-, and 180-days of discharge in pediatric patients with anxiety and depressive disorders using demographic, clinical, and pharmacogenetic data in the electronic health record. Multiple algorithms will be evaluated to determine the best predictive model for each outcome. Important factors influencing readmission and model performance for each outcome will be assessed and compared. Additionally, this will be the first machine learning evaluation of psychiatric readmission in pediatric patients. The second aim will assess the generalizability of our models using external pediatric psychiatric admission data from a comparable institution. This validation is significant to ensure our model is applicable to new patients if this were to be implemented clinically to improve patient care. The exploratory third aim of this proposal will assess the ability of a model to select commonly prescribed antidepressant medications that reduce readmission risk. The model will predict the risk of readmission if a patient had been prescribed each antidepressant, which will be compared to current prescribing practices. This will evaluate the impact of antidepressants on future psychiatric readmission, which could aid in medication selection. This project will be the first to evaluate psychiatric readmission in children and adolescents through a machine learning approach, with the goal to reduce psychiatric readmission, thereby improving patient care and quality of life. Further, this research will lay the foundation for future studies evaluating additional data modalities and outcomes as we move towards more personalized treatments and recommendations for pediatric patients with mental health disorders.