Predicting Psychiatric Readmission with Machine Learning in Children and Adolescents

通过机器学习预测儿童和青少年的精神病再入院

基本信息

批准号：
10710526
负责人：
Ethan Andrew Poweleit
金额：
$ 4.12万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-19 至 2024-09-18
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10710526
关键词：
Academic Medical Centers Address Adherence Admission activity Adolescent Algorithms Antidepressive Agents Anxiety Anxiety Disorders Applications Grants Child Child Mental Health Childhood Clinical Clinical Data Data Data Set Depressed mood Depressive disorder Development Disease Disease remission Drug Kinetics Electronic Health Record Emergency Situation Ensure Evaluation Family Foundations Future Genes Goals Hospital Costs Hospitalization Hospitalized Child Institution Length of Stay Machine Learning Medical center Mental Depression Mental Health Mental disorders Modality Modeling Outcome Patient Care Patient Readmission Patient-Focused Outcomes Patients Pediatric Hospitals Performance Pharmaceutical Preparations Pharmacogenetics Population Prediction of Response to Therapy Process Psychiatric therapeutic procedure Psychiatry Quality of life Recommendation Research Research Training Risk Selection for Treatments Structure Testing Training Treatment outcome United States Validation Work Youth anxiety reduction anxiety symptoms anxious behavioral impairment clinical care cost demographics depressed patient depressive symptoms genetic information high dimensionality hospital readmission improved machine learning algorithm machine learning model patient population pediatric patients personalized medicine precision medicine prediction algorithm predictive modeling readmission risk research and development response risk prediction side effect targeted treatment treatment planning treatment response treatment risk

项目摘要

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.

项目摘要/摘要心理健康障碍，包括焦虑和抑郁症，在儿科患者中很常见损害行为功能和生活质量。对于患有严重疾病的人，患者可能会住院有针对性的治疗。尽管有药物和/或治疗治疗，但儿童和青少年经常是由于许多原因，包括治疗无效，药物治疗，入学到精神病护理副作用以及遵守该疾病治疗计划的问题。实际上，有25％的年轻人被重新入学出院一年之内。此外，这些疾病的治疗对患者而言可能是长期且昂贵的，他们的家人，特别是如果患者住院或重新住院，患者忍受多种药物在找到最好的药物之前进行试验。为了通过小儿精神病再入院解决这些问题，这项研究的重点是开发机器学习算法以预测精神病再入学在儿童和青少年中。拟议研究的第一个目的是开发和建立机器学习算法以预测焦虑和焦虑的儿科患者的30、90和180天内的精神病再入院电子健康记录中使用人口统计学，临床和药物遗传学数据的抑郁症。将评估多种算法，以确定每个结果的最佳预测模型。重要因素将评估和比较每个结果的影响入再入院和模型绩效。此外，这将是对儿科患者进行精神病再入院的第一次机器学习评估。第二个目标将使用外部儿科精神病学数据来评估我们的模型的普遍性可比机构。此验证对于确保我们的模型适用于新患者是重要的在临床上实施以改善患者护理。该提案的探索性第三个目标将评估模型选择常规规定的能力抗抑郁药可降低再入院风险。该模型将预测再入院的风险患者已被处方，每种抗抑郁药，这将与当前的处方实践进行比较。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，以减少精神病再入院的目标，从而改善患者护理和质量生活。此外，这项研究将为未来的研究奠定基础，以评估其他数据方式和当我们向儿科患者提供更多个性化治疗和建议时，结果心理健康障碍。