Department of Psychology, University of Nevada, Las Vegas, Las Vegas (Chen, Kraus); Boys and Girls Clubs of Southern Nevada, Las Vegas (M. J. Freeman); Inspiring Children Foundation, Las Vegas (A. J. Freeman).
Psychiatr Serv. 2023 Sep 1;74(9):943-949. doi: 10.1176/appi.ps.20220201. Epub 2023 Mar 14.
The authors used a machine-learning approach to model clinician decision making regarding psychiatric hospitalization of children and youths in crisis and to identify factors associated with the decision to hospitalize.
Data consisted of 4,786 mobile crisis response team assessments of children and youths, ages 4.0-19.5 years (mean±SD=14.0±2.7 years, 56% female), in Nevada. The sample assessments were split into training and testing data sets. A random-forest machine-learning algorithm was used to identify variables related to the decision to hospitalize a child or youth after the crisis assessment. Results from the training sample were externally validated in the testing sample.
The random-forest model had good performance (area under the curve training sample=0.91, testing sample=0.92). Variables found to be important in the decision to hospitalize a child or youth were acute suicidality, followed by poor judgment or decision making, danger to others, impulsivity, runaway behavior, other risky behaviors, nonsuicidal self-injury, psychotic or depressive symptoms, sleep problems, oppositional behavior, poor functioning at home or with peers, depressive or schizophrenia spectrum disorders, and age.
In crisis settings, clinicians were found to mostly focus on acute factors that increased risk for danger to self or others (e.g., suicidality, poor judgment), current psychiatric symptoms (e.g., psychotic symptoms), and functioning (e.g., poor home functioning, problems with peer relationships) when deciding whether to hospitalize or stabilize a child or youth. To reduce psychiatric hospitalization, community-based services should target interventions to address these important factors associated with the need for a higher level of care among youths in psychiatric crisis.
作者采用机器学习方法对精神科医生对处于危机中的儿童和青少年进行精神病住院治疗的决策进行建模，并确定与住院决策相关的因素。
数据来自内华达州的 4786 例移动危机反应小组对 4.0-19.5 岁（平均±SD=14.0±2.7 岁，56%为女性）儿童和青少年的评估。将样本评估分为训练和测试数据集。随机森林机器学习算法用于识别危机评估后与儿童或青少年住院决定相关的变量。训练样本的结果在测试样本中进行了外部验证。
随机森林模型表现良好（训练样本曲线下面积=0.91，测试样本=0.92）。在决定是否住院治疗儿童或青少年时，发现有重要意义的变量是急性自杀意念，其次是判断力或决策力差、对他人有危险、冲动、离家出走行为、其他危险行为、非自杀性自伤、精神病或抑郁症状、睡眠问题、对立行为、在家或与同伴的功能差、抑郁或精神分裂症谱系障碍以及年龄。
在危机环境中，临床医生在决定是否住院或稳定儿童或青少年时，主要关注增加自伤或对他人造成危险的急性因素（例如自杀意念、判断力差）、当前的精神科症状（例如精神病症状）和功能（例如家庭功能差、与同伴关系问题）。为了减少精神科住院治疗，基于社区的服务应针对这些与青少年在精神科危机中需要更高水平护理相关的重要因素进行干预。
