Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. Increasing evidence suggests that depression network connectivity may be a risk factor for transitioning and sustaining a depressive state. Here we analysed social media (Twitter) data from 946 participants who retrospectively self-reported the dates of any depressive episodes in the past 12 months and current depressive symptom severity. We construct personalised, within-subject, networks based on depression-related linguistic features. We show an association existed between current depression severity and 8 out of 9 text features examined. Individuals with greater depression severity had higher overall network connectivity between depression-relevant linguistic features than those with lesser severity. We observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode. The connectivity within personalized networks of depression-associated linguistic features may change dynamically with changes in current depression symptoms.
Sexually Transmitted Infections: Compelling Case for an Improved Screening Strategy Stephen Hull, MHS, Seán Kelley, MD, MSc, and Janice L. Clarke, RN, BBA Editorial: Sexually Transmitted Infections-A Fixable Problem: David B. Nash, MD, MBA S-3 Introduction S-3 Rising Prevalence of Sexually Transmitted Diseases (STIs) S-4 Current Screening Rates for Chlamydia and Gonorrhea S-4 The Human Toll and Economic Burden of STI-Related Illness S-5 Current Screening Guidelines for Chlamydia and Gonorrhea S-5 Factors Contributing to Inadequate Screening, Diagnosis, and Treatment for STIs S-6 Methods Used to Improve Screening Rates S-7 Benefits of Opt-Out Screening Strategies for STIs S-8 Cost-Effectiveness of Screening for STIs S-8 Discussion S-9 Conclusion S-10.
Depressed individuals use language differently than healthy controls and it has been proposed that social media posts can be used to identify depression. Much of the evidence behind this claim relies on indirect measures of mental health and few studies have tested if these language features are specific to depression versus other aspects of mental health. We analysed the Tweets of 1006 participants who completed questionnaires assessing symptoms of depression and 8 other mental health conditions. Daily Tweets were subjected to textual analysis and the resulting linguistic features were used to train an Elastic Net model on depression severity, using nested cross-validation. We then tested performance in a held-out test set (30%), comparing predictions of depression versus 8 other aspects of mental health. The depression trained model had modest out-of-sample predictive performance, explaining 2.5% of variance in depression symptoms (R2 = 0.025, r = 0.16). The performance of this model was as-good or superior when used to identify other aspects of mental health: schizotypy, social anxiety, eating disorders, generalised anxiety, above chance for obsessive-compulsive disorder, apathy, but not significant for alcohol abuse or impulsivity. Machine learning analysis of social media data, when trained on well-validated clinical instruments, could not make meaningful individualised predictions regarding users’ mental health. Furthermore, language use associated with depression was non-specific, having similar performance in predicting other mental health problems.
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