Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil; Universidade Federal do Rio Grande do Sul, School of Medicine, Graduate Program in Psychiatry and Behavioral Sciences, Department of Psychiatry, Porto Alegre, RS, Brazil; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil.
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
Psychiatry Res. 2023 Oct;328:115404. doi: 10.1016/j.psychres.2023.115404. Epub 2023 Aug 6.
Major Depressive Disorder and Bipolar Disorder are psychiatric disorders associated with psychosocial impairment. Despite clinical improvement, functional complaints usually remain, mainly impairing occupational and cognitive performance. The aim of this study was to use machine learning techniques to predict functional impairment in patients with mood disorders. For that, analyzes were performed using a population-based cohort study. Participants diagnosed with a mood disorder at baseline and reassessed were considered (n = 282). Random forest (RF) with previous recursive feature selection and LASSO algorithms were applied to a training set with imputed data by bagged trees resulting in two main models. Following recursive feature selection, 25 variables were retained. The RF model had the best performance compared to LASSO. The most important variables in predicting functional impairment were sexual abuse, severity of depressive, anxiety, and somatic symptoms, physical neglect, emotional abuse, and physical abuse. The model demonstrated acceptable performance to predict functional impairment. However, our sample is composed of young participants and the model may not generalize to older individuals with mood disorders. More studies are needed in this direction. The presented calculator has clinical, sociodemographic, and environmental data, demonstrating that it is possible to use such information to predict functional performance.
重性抑郁障碍和双相情感障碍是与社会心理障碍相关的精神障碍。尽管临床症状有所改善，但功能障碍通常仍然存在，主要影响职业和认知表现。本研究旨在使用机器学习技术预测心境障碍患者的功能障碍。为此，使用基于人群的队列研究进行了分析。考虑了基线时被诊断为心境障碍并重新评估的参与者（n=282）。随机森林（RF）结合先前的递归特征选择和 LASSO 算法应用于袋装树进行插补数据的训练集，得出了两个主要模型。经过递归特征选择，保留了 25 个变量。与 LASSO 相比，RF 模型的性能最佳。预测功能障碍的最重要变量是性虐待、抑郁、焦虑和躯体症状的严重程度、身体忽视、情感虐待和身体虐待。该模型在预测功能障碍方面表现出可接受的性能。然而，我们的样本由年轻参与者组成，该模型可能不适用于患有心境障碍的老年个体。需要在这方面开展更多的研究。该计算器提供了临床、社会人口统计学和环境数据，表明可以使用这些信息来预测功能表现。
