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University students are experiencing a mental health crisis. COVID-19 has exacerbated this situation. We have surveyed students in 2 universities in Lebanon to gauge their mental health challenges. We have constructed a machine learning (ML) approach to predict symptoms of depression, anxiety, and stress based on demographics and self-rated health measures. Our approach involved developing 8 ML predictive models, including Logistic Regression (LR), multi-layer perceptron (MLP) neural network, support vector machine (SVM), random forest (RF) and XGBoost, AdaBoost, Naïve Bayes (NB), and K-Nearest neighbors (KNN). Following their construction, we compared their respective performances. Our evaluation shows that RF (AUC = 78.27%), NB (AUC = 76.37%), and AdaBoost (AUC = 72.96%) have provided the highest-performing AUC scores for depression, anxiety, and stress, respectively. Self-rated health is found to be the top feature in predicting depression, while age was the top feature in predicting anxiety and stress, followed by self-rated health. Future work will focus on using data augmentation approaches and extending to multi-class anxiety predictions., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background : Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results : The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew's Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features' importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Qasrawi R et al.)

Purpose: Adolescent refugees are at risk of mental health disorders and underdiagnosed risky behaviors. Limited research exists in the Middle East and North Africa. This study aims to assess psychosocial wellbeing and risk-taking behaviors among adolescent refugees displaced to South Beirut following a standardized framework. Methods: A cross-sectional study using face-to-face confidential HEEADSSS (Home, Education/Employment, Eating, Activities, Drugs, Sexuality, Safety and Suicide/Depression) interviews was conducted among 52 Syrian adolescent refugees, between the ages of 14 and 21, in a health center in South Beirut. Results: The mean age of the interviewees was 17.04 ± 1.77 years, with a male predominance 34 (65.4%). Five (9.6%) were married, 38 (73.1%) were not attending school 27 (52.9%) lived in a place with a crowding index =3.5 and 21 (40.4%) were working. Risky health concerns or behaviors detected included no activities or exercise 38 (73.1%), eating one to two meals per day 39 (75%) and smoking 22 (42.3%). Eleven (21.2%) have been ever offered drugs and 22 (42.3%) believed they should carry a weapon for protection. Twenty one out of 32 (65.7%) had major depressive disorders and 33 (63.5%) screened positive for behavioral problems. Exposure to home verbal or physical violence, male gender, smoking, and employment were associated with high scoring in behavioral problems. Smoking and ever been touched in an unwanted way were found to be associated with depression. Conclusion and practical implications: Implementing the HEEADSSS interviewing assessment within medical encounters with refugee adolescents is one efficient way to detect risky health behaviors and mental health problems. Interventions need to be implemented as early as possible in the refugees' journey to help them cope and gain resilience. Training health care providers to conduct the questionnaire and delivering brief counseling when required is recommended. Establishing a network of referrals to provide multidisciplinary care to adolescents can be helpful. Obtaining a fund to distribute safety helmets for adolescent motorbike drivers can be a way to reduce injuries. More research among adolescent refugees in multiple settings, including teenagers in the host country, is needed to serve this population better. [ABSTRACT FROM AUTHOR]

University students are experiencing a mental health crisis across the world. COVID-19 has exacerbated this situation. We have conducted a survey among university students in two universities in Lebanon to gauge mental health challenges experienced by students. We constructed a machine learning approach to predict anxiety symptoms among the sample of 329 respondents based on student survey items including demographics and self-rated health. Five algorithms including logistic regression, multi-layer perceptron (MLP) neural network, support vector machine (SVM), random forest (RF) and XGBoost were used to predict anxiety. Multi-Layer Perceptron (MLP) provided the highest performing model AUC score (AUC=80.70%) and self-rated health was found to be the top ranked feature to predict anxiety. Future work will focus on using data augmentation approaches and extending to multi-class anxiety predictions. Multidisciplinary research is crucial in this emerging field. [ABSTRACT FROM AUTHOR]