Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students.

Background: A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated. Methods: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks. Results: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia. Conclusion: These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder. Trial registration: Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

Linguistic markers of anxiety and depression in Somatic Symptom and Related Disorders: Observational study of a digital intervention.

BACKGROUND: Somatic Symptom and Related Disorders (SSRD), including chronic pain, result in frequent primary care visits, depression and anxiety symptoms, and diminished quality of life. Treatment access remains limited due to structural barriers and functional impairment. Digital delivery offers to improve access and enables transcript analysis via Natural Language Processing (NLP) to inform treatment. Therefore, we investigated asynchronous message-delivered SSRD treatment, and used NLP methods to identify symptom reduction markers from emotional valence. METHODS: 173 individuals diagnosed with SSRD received interventions from licensed therapists via messaging 5 days/week for 8 weeks. Depression and anxiety symptoms were measured with the PHQ-9 and GAD-7 from baseline every three weeks. Symptoms trajectories were identified using unsupervised random forest clustering. Emotional valence expressed and use of emotional words were extracted from patients' de-identified transcripts, respectively using VADER and NCR Lexicon. Valence differences were examined using logistic regression. RESULTS: Two subpopulations were identified showing symptoms Improvement (n = 72; 41.62 %) and non-response (n = 101; 58.38 %). Improvement patients expressed more positive valence in the first week of treatment (OR = 1.84, CI: 1.12-3.02; p = .015) and were less likely to express negative valence by the end of treatment (OR = 0.05; CI: 0.30-0.83; p = .008). Non-response patients used more negative valence words, including pain. LIMITATIONS: Findings were derived from observational data obtained during an ecological intervention, without the inclusion of a control group. CONCLUSIONS: NLP identified linguistic markers distinguishing changes in anxiety and depression symptoms over treatment. Digital interventions offer new forms of delivery and provide the opportunity to automatically collect data for linguistic analysis.

Networks of major depressive disorder: A systematic review.

There has been a marked increase of network studies of Major Depressive Disorder (MDD). Despite rapidly growing contributions, their findings have yet to be systematically aggregated and examined. We therefore conducted a systematic review of depression network studies using PRISMA guidelines. A total of 254 clinical and population studies were collected from ISI's Web of Science and PsycINFO, between January 2010 to May 2020. A total of 23 between-subject studies were included for review, resulting in 58 cross-sectional networks. To determine their most critical symptoms and their connections, we analyzed strength centrality rankings, and aggregated the most robust symptoms connections into a summary network. Results indicated substantial variability between study samples, depression measures, and network features. Fatigue and Depressed Mood were the most central symptoms, while Weight changes tended to have the weakest centrality. Depressed Mood and Fatigue formed two separated symptoms communities characterized by recurrent connections, with Mood-Anhedonia as the most frequent edge of MDD. Network analysis informed our understanding of MDD, suggesting the critical role of Fatigue and Depressed Mood. The study's findings are discussed in their clinical and methodological implications, including future directions for network studies of MDD.