Decoding rumination: A machine learning approach to a transdiagnostic sample of outpatients with anxiety, mood and psychotic disorders

Psychological well-being, risk factors, and coping strategies with social isolation and new challenges in times of adversity caused by the COVID-19 pandemic

“…Individuals using this type of maladaptive emotion regulation strategy are concentrating on their negative feelings and thoughts related to the current situation ( Kraaij & Garnefski, 2019 ). Rumination has been shown to be responsible for the high comorbidity of several mental disorder diagnoses, e.g., anxiety, mood, and psychotic disorders ( Balzarotti et al, 2016 ; Silveira et al, 2020 ). This is in line with a recent study ( Satici et al, 2020 ) showing that rumination mediated the connection between intolerance of uncertainty and fear of COVID-19 because the focus on negative emotions impacts mental well-being.…”

Rumination, automatic thoughts, dysfunctional attitudes, and thought suppression as transdiagnostic factors in depression and anxiety

“…Even though various models of rumination have been conceptualized (Koster et al 2011;Krys et al 2020;Miller et al 2020;Ricarte et al 2018;Watkins and Roberts 2020), as the most influential notion, response styles theory defines rumination as patterns of passively and pervasively thinking about one's emotional symptoms as well as the causes and consequences of these symptoms (Lyubomirsky et al 2015). A tendency to ruminate about one's problems and emotions is relatively stable over time and contributes to perseveration of negative affective states (Silveira et al 2020;Whisman et al 2020), particularly self-focused rumination (Bagby et al 2004). Rumination may lead to negative emotional states through different mechanisms that ruminative thinking is a significant correlate of more dysfunctional information processing (Kaiser et al 2019;Kaiser et al 2018), over-focusing on negative aspects of a stressful situation (Yasinski et al 2016), less effective problem solving (Jones et al 2017), failure in getting social support (Hasegawa et al 2018;Wang et al 2019), and difficulties in taking in action for active coping with problems (Nolen-Hoeksema et al 1994).…”

Chinese College Students Have Higher Anxiety in New Semester of Online Learning During COVID-19: A Machine Learning Approach

“…Machine learning can deal with big data in high velocity and a variety of forms, so it has been widely implemented in accurately predicting mental health problems, such as anxiety, depression, obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) (Kumar et al, 2020;Silveira et al, 2020;Tennenhouse et al, 2020;Xing et al, 2020); classification or diagnosis (Peng et al, 2013;Thabtah, 2018); predicting self-harm and imputing its presence as a missing phenotype (Kumar et al, 2020); and also in distinguishing patients with bipolar disorder from healthy individuals with neuroimaging (Mwangi et al, 2016), neurocognitive data (Wu et al, 2016(Wu et al, , 2017a, and serum biomarkers (Pinto et al, 2017). This technique includes pattern recognition through the use of complex computational algorithms fed by large data and has the potential to create a paradigm shift in the prediction and stratification of clinical outcomes (Passos et al, 2016;Librenza-Garcia et al, 2017;Silveira et al, 2020). As reported by Ge et al (2020), machine learning can be used in predicting later clinical outcomes by combining multiple pieces of information from different domains in an effective way and allowing the identification of the most predictive combination of domains.…”