权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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/摘要精神健康障碍，包括焦虑和抑郁，在儿科患者中很常见，而且显著损害行为功能和生活质量。对于那些病情严重的患者，患者可能会住院治疗更多时间。有针对性的治疗。尽管接受药物和/或治疗，但儿童和青少年经常由于多种原因重新进入精神病治疗，包括治疗无效、药物治疗副作用，以及与坚持治疗计划有关的问题。事实上，25%的青年被重新接纳在退伍后一年内。此外，这些疾病的治疗对患者来说可能是漫长和昂贵的，他们的家人，特别是在患者住院或再次住院的情况下，患者忍受多种药物治疗在找到最好的药物之前进行试验。为了解决儿科精神科重新入院的这些问题，这项研究的重点是开发一种机器学习算法来预测精神病患者的再入院情况在儿童和青少年中。拟议研究的第一个目标是开发和建立机器学习算法来预测儿科焦虑症患者出院后30天、90天和180天内再次精神科住院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的抑郁症。将对多种算法进行评估，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种结果对再入院和模型性能的影响。另外，这将是第一次对儿科患者的精神科再入院进行机器学习评估。第二个目标将使用外部儿科精神科入院数据评估我们模型的普适性类似的机构。这一验证对于确保我们的模型适用于新患者具有重要意义将在临床上实施，以改善患者护理。本提案的探索性第三个目标将评估模型选择共同处方的能力降低再入院风险的抗抑郁药物。该模型将预测重新入院的风险如果已经给病人开了每种抗抑郁药，这将与目前的处方做法进行比较。这将评估抗抑郁药物对未来精神科再入院的影响，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科再住院的项目。学习方法，目的是减少精神病患者的再入院，从而改善患者的护理和质量生活的一部分。此外，这项研究将为未来评估更多数据模式和随着我们走向更个性化的治疗和对儿童患者的建议，结果精神健康障碍。