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Decoding rumination: A machine learning approach to a transdiagnostic sample of outpatients with anxiety, mood and psychotic disorders.
Journal of Psychiatric Research ( IF 4.8 ) Pub Date : 2019-12-07 , DOI: 10.1016/j.jpsychires.2019.12.005 Érico de Moura Silveira 1 , Ives Cavalcante Passos 1 , Jan Scott 2 , Giovana Bristot 3 , Ellen Scotton 1 , Lorenna Sena Teixeira Mendes 4 , Ana Claudia Umpierre Knackfuss 4 , Luciana Gerchmann 4 , Adam Fijtman 1 , Andrea Ruschel Trasel 1 , Giovanni Abrahão Salum 4 , Márcia Kauer-Sant'Anna 1
Journal of Psychiatric Research ( IF 4.8 ) Pub Date : 2019-12-07 , DOI: 10.1016/j.jpsychires.2019.12.005 Érico de Moura Silveira 1 , Ives Cavalcante Passos 1 , Jan Scott 2 , Giovana Bristot 3 , Ellen Scotton 1 , Lorenna Sena Teixeira Mendes 4 , Ana Claudia Umpierre Knackfuss 4 , Luciana Gerchmann 4 , Adam Fijtman 1 , Andrea Ruschel Trasel 1 , Giovanni Abrahão Salum 4 , Márcia Kauer-Sant'Anna 1
Affiliation
OBJECTIVE
To employ machine learning algorithms to examine patterns of rumination from RDoC perspective and to determine which variables predict high levels of maladaptive rumination across a transdiagnostic sample.
METHOD
Sample of 200 consecutive, consenting outpatient referrals with clinical diagnoses of schizophrenia, schizoaffective, bipolar, depression, anxiety disorders, obsessive compulsive and post-traumatic stress. Machine learning algorithms used a range of variables including sociodemographics, serum levels of immune markers (IL-6, IL-1β, IL-10, TNF-α and CCL11) and BDNF, psychiatric symptoms and disorders, history of suicide and hospitalizations, functionality, medication use and comorbidities.
RESULTS
The best model (with recursive feature elimination) included the following variables: socioeconomic status, illness severity, worry, generalized anxiety and depressive symptoms, and current diagnosis of panic disorder. Linear support vector machine learning differentiated individuals with high levels of rumination from those ones with low (AUC = 0.83, sensitivity = 75, specificity = 71).
CONCLUSIONS
Rumination is known to be associated with poor prognosis in mental health. This study suggests that rumination is a maladaptive coping style associated not only with worry, distress and illness severity, but also with socioeconomic status. Also, rumination demonstrated a specific association with panic disorder.
中文翻译:
解码反省:一种机器学习方法,用于对焦虑,情绪和精神病患者进行门诊诊断。
目的采用机器学习算法从RDoC角度检查反刍模式,并确定哪些变量可预测跨诊断样本的高水平不良适应性反刍。方法抽取200例经过连续同意的门诊患者进行转诊,并进行临床诊断为精神分裂症,精神分裂症,躁郁症,抑郁症,焦虑症,强迫症和创伤后应激反应。机器学习算法使用了一系列变量,包括社会人口统计学,免疫标志物的血清水平(IL-6,IL-1β,IL-10,TNF-α和CCL11)和BDNF,精神病症状和疾病,自杀史和住院情况,功能,药物使用和合并症。结果最佳模型(具有递归特征消除功能)包括以下变量:社会经济状况,疾病严重程度,担忧,广泛性焦虑和抑郁症状,以及当前的恐慌症诊断。线性支持向量机学习将反刍力高的人与反刍力高的人区分开(AUC = 0.83,灵敏度= 75,特异性= 71)。结论认为反刍与精神健康预后不良有关。这项研究表明,反省是一种适应不良的应对方式,不仅与忧虑,困扰和疾病的严重程度有关,而且与社会经济地位有关。同样,反刍证明与恐慌症有特定的联系。结论认为反刍与精神健康预后不良有关。这项研究表明,反省是一种适应不良的应对方式,不仅与忧虑,困扰和疾病的严重程度有关,而且与社会经济地位有关。同样,反刍证明与恐慌症有特定的联系。结论认为反刍与精神健康预后不良有关。这项研究表明,反省是一种适应不良的应对方式,不仅与忧虑,困扰和疾病的严重程度有关,而且与社会经济地位有关。同样,反刍证明与恐慌症有特定的联系。
更新日期:2019-12-07
中文翻译:
解码反省:一种机器学习方法,用于对焦虑,情绪和精神病患者进行门诊诊断。
目的采用机器学习算法从RDoC角度检查反刍模式,并确定哪些变量可预测跨诊断样本的高水平不良适应性反刍。方法抽取200例经过连续同意的门诊患者进行转诊,并进行临床诊断为精神分裂症,精神分裂症,躁郁症,抑郁症,焦虑症,强迫症和创伤后应激反应。机器学习算法使用了一系列变量,包括社会人口统计学,免疫标志物的血清水平(IL-6,IL-1β,IL-10,TNF-α和CCL11)和BDNF,精神病症状和疾病,自杀史和住院情况,功能,药物使用和合并症。结果最佳模型(具有递归特征消除功能)包括以下变量:社会经济状况,疾病严重程度,担忧,广泛性焦虑和抑郁症状,以及当前的恐慌症诊断。线性支持向量机学习将反刍力高的人与反刍力高的人区分开(AUC = 0.83,灵敏度= 75,特异性= 71)。结论认为反刍与精神健康预后不良有关。这项研究表明,反省是一种适应不良的应对方式,不仅与忧虑,困扰和疾病的严重程度有关,而且与社会经济地位有关。同样,反刍证明与恐慌症有特定的联系。结论认为反刍与精神健康预后不良有关。这项研究表明,反省是一种适应不良的应对方式,不仅与忧虑,困扰和疾病的严重程度有关,而且与社会经济地位有关。同样,反刍证明与恐慌症有特定的联系。结论认为反刍与精神健康预后不良有关。这项研究表明,反省是一种适应不良的应对方式,不仅与忧虑,困扰和疾病的严重程度有关,而且与社会经济地位有关。同样,反刍证明与恐慌症有特定的联系。