Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO.

A better understanding of the relationships between 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 which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated. 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. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD). 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 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes. These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression. Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Polyregulation refers to the simultaneous or sequential use of emotion regulation (ER) approaches. Although research has documented that polyregulation is common, more research is needed to clarify when it occurs and how it relates to ER processes. A total of 179 adults reported on a recent event 5×/day for 14 days between 2022 and 2023. For each event, participants reported on momentary contextual factors, including positive affect and negative affect, social context (presence of others), and event appraisals (pleasantness, importance), along with whether they engaged in ER. When ER was reported, participants indicated whether they used single-strategy ER or various types of polyregulation and their perceived ER effort, difficulty, and success. Polyregulation was common (53%) and endorsed more frequently than single-strategy ER (47%), though the magnitude of this effect was small (p = .046, Wilcoxon effect size r = .16). Single-strategy ER use was more frequent than any one type of polyregulation approach. The likelihood of polyregulation was higher when individuals felt more negative affect, perceived events as more important, and were with others (vs. alone). Positive affect and pleasantness were not robust predictors of polyregulation. Compared to single-strategy ER, polyregulation was associated with greater ER effort and difficulty, but not success. The polyregulation approach "redo" (i.e., switching strategies after the first way did not work) was, however, associated with less ER success compared to other forms of ER, including single-strategy ER. Altogether, polyregulation was frequent in daily life and varied based on contextual factors. Engaging in polyregulation may be more demanding than using a single strategy. (PsycInfo Database Record (c) 2026 APA, all rights reserved).