Predicting Psychiatric Readmission with Machine Learning in Children and Adolescents

项目总结/摘要 心理健康障碍，包括焦虑和抑郁，在儿科患者中很常见， 损害行为功能和生活质量。对于那些病情严重的患者， 针对性治疗。尽管有药物和/或治疗，儿童和青少年经常 由于许多原因重新进入精神病护理，包括治疗无效，药物治疗， 副作用，以及坚持治疗计划的问题。事实上，25%的年轻人 出院后一年内。此外，对这些病症的治疗对患者来说可能是长期和昂贵的， 他们的家人，特别是如果患者住院或再次住院，患者接受多种药物治疗 在找到最好的药物之前进行试验。为了解决这些问题与儿科精神病再入院， 这项研究的重点是开发一种机器学习算法来预测精神病再入院。 在儿童和青少年中。 该研究的第一个目标是开发和建立机器学习算法来预测 在出院后30天、90天和180天内，患有焦虑和抑郁症的儿科患者的精神病再入院率 使用电子健康记录中的人口统计学、临床和药物遗传学数据评估抑郁症。 将对多种算法进行评估，以确定每种结局的最佳预测模型。重要因素 将评估和比较每个结果的影响再入院和模型性能。此外，本发明还 这将是第一次对儿科患者的精神病再入院进行机器学习评估。第二个目的 将评估我们的模型的普遍性，使用外部儿科精神病入院数据，从一个 类似的机构。该验证对于确保我们的模型适用于新患者具有重要意义， 在临床上实施，以改善患者护理。 本提案的第三个探索性目标将评估模型选择常用处方的能力 降低再入院风险的抗抑郁药物。该模型将预测再入院的风险，如果 患者已被处方每种抗抑郁药，将与当前处方实践进行比较。这 将评估抗抑郁药对未来精神病再入院的影响，这可能有助于药物治疗。 选择. 该项目将是第一个通过机器评估儿童和青少年精神病再入院的项目 学习方法，目标是减少精神病再入院，从而改善患者护理和质量 生命此外，这项研究将为未来评估其他数据模式的研究奠定基础， 随着我们朝着更个性化的治疗和建议儿科患者的结果， 心理健康障碍

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.