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：
10604849
负责人：
Ethan Andrew Poweleit
金额：
$ 4万
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/10604849
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 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 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 anxious behavioral impairment case control clinical care cost demographics depressed patient depressive symptoms genetic information high dimensionality hospital readmission improved ineffective therapies machine learning algorithm machine learning model patient population pediatric patients personalized medicine precision medicine prediction algorithm predictive modeling readmission risk research and development response 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.

项目概要/摘要心理健康障碍，包括焦虑和抑郁，在儿科患者中很常见，并且显着损害行为功能和生活质量。对于病情严重的患者，可能需要住院治疗更长的时间针对性治疗。尽管进行药物和/或治疗，儿童和青少年仍经常由于多种原因，包括治疗无效、药物治疗等，重新进入精神科护理副作用以及遵守疾病治疗计划的问题。事实上，25% 的青少年重新入学出院后一年内。此外，这些疾病的治疗对于患者和患者来说可能是漫长且昂贵的。他们的家人，特别是当患者住院或再次住院且患者正在承受多种药物治疗时在找到最佳药物之前进行试验。为了解决儿科精神病再入院的这些问题，这项研究的重点是开发机器学习算法来预测精神病再入院在儿童和青少年中。该研究的首要目标是开发和建立机器学习算法来预测患有焦虑和焦虑症的儿科患者在出院 30、90 和 180 天内再次入院使用电子健康记录中的人口统计、临床和药物遗传学数据来治疗抑郁症。将评估多种算法以确定每种结果的最佳预测模型。重要因素将评估和比较每个结果的再入院影响和模型表现。此外，这将是首次对儿科患者的精神病再入院进行机器学习评估。第二个目标将使用来自外部儿科精神病入院数据来评估我们模型的普遍性同类机构。此验证对于确保我们的模型适用于新患者（如果是这样的话）具有重要意义。应用于临床以改善患者护理。该提案的探索性第三个目标将评估模型选择常用规定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，如果患者已服用每种抗抑郁药，并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次入院的影响，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情况的项目学习方法，旨在减少精神病患者的再入院，从而改善患者护理和质量的生活。此外，这项研究将为未来评估其他数据模式和随着我们为儿科患者提供更加个性化的治疗和建议，我们会取得更好的结果心理健康障碍。