A simulation-based network analysis of intervention targets for comorbid symptoms of depression and anxiety in Chinese healthcare workers in the post-dynamic zero-COVID policy era by Chao Zhang, Ruyong Li, Wei Zhang, Yanqiang Tao, Xiangping Liu, Yichao Lv

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Machine learning-based prediction of knee pain risk using lipid metabolism biomarkers: a prospective cohort study from CHARLS by Biao Guo, Yuan Li, Yuan Li, Weihang Peng, Yabin Liu, Fei He, Zhe Zhai

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DISEASES OF THE CARDIOVASCULAR SYSTEM – PREVENTIVE AND EDUCATIONAL ACTIVITIES WITHIN THE COUNTY PROGRAM OF PROPHYLAXIS AND PREVENTION OF HEALTH “TOGETHER FOR THE HEART” AMONG THE I... by Anna Ślifirczyk

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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

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Machine Learning Predictive Models for Survival in Patients with Brain Stroke by Solmaz Norouzi, Samira Ahmadi, Shayeste Alinia, Farshid Farzipoor, Azadeh Shahsavari, Ebrahim Hajizadeh, Mohammad Asghari Jafarabadi

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THE EUROPEAN SOCIETY FOR CLINICAL AND ECONOMIC ASPECTS OF OSTEOPOROSIS AND OSTEOARTHRITIS (ESCEO) ALGORITHM FOR THE MANAGEMENT OF KNEE OSTEOARTHRITIS IS APPLICABLE TO RUSSIAN CLINI... by L. N. Denisov, E. S. Tsvetkova, G. Sh. Golubev, O. V. Bugrova, I. S. Dydykina, A. I. Dubikov, L. V. Menshikova, L. K. Peshekhonova, A. P. Rebrov, A. N. Torgashin, E. A. Trofimov, S. P. Yakupova, E. V. Zonova, O. Brewer, C. Cooper, J. Reginster, L. A. Knyazeva

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Network relationship analysis of the influencing factors and disease risk prediction for hyperlipidemia in the elderly population of Henan province based on a Bayesian network mode... by Wenjuan WANG, Hongji ZENG, Yahui LIU, Shufan WEI, Rui WANG, Qingfeng TIAN

“…Based on multivariate unconditional logistic regression analysis, a hyperlipidemia Bayesian network model was constructed using the Max-Min Hill-Climbing (MMHC) algorithm to analyze the network relationship of influencing factors for hyperlipidemia in the local elderly population and predict their disease risk. …”
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Geoecological Analysis of the Effects of Pipeline Transport on the Environment of Transit Regions (Voronezh Region, Russia) by L. A. Mezhova, A. M. Lugovskoy, Yu. N. Gladkiy, A. B. Glazyeva, O. Yu. Sushkova, L. B. Vampilova, A. A. Sokolova, L. A. Lugovskaya

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dmQAR: Mapping Metacognition in Digital Spaces onto Question–Answer Relationship by Brittany Adams, Nance S. Wilson, Gillian E. Mertens

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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
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Postpartum depression in Northeastern China: a cross-sectional study 6 weeks after giving birth by XuDong Huang, LiFeng Zhang, ChenYang Zhang, Jing Li, ChenYang Li

“…PPD was screened using the Edinburgh Postnatal Depression Scale (EPDS, score ≥ 9). Key risk factors were identified through machine learning techniques, including LASSO regression and the Boruta algorithm, and their associations were evaluated using logistic regression. …”
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Stroke in children and adolescents: topical problems of pre-hospital diagnostics by Y. A. Khachaturov, I. O. Shchederkina, N. F. Plavunov, A. M. Sidorov, E. E. Petryaykina, I. P. Vytkovskaya, V. A. Kadyshev

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Predicting Student Performance through Machine Learning Methods: Naive Bayesian Classifier by Chenyang Li, Qianzhi Zhou, Luming Du, Sihan Zhang

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Identification оf World Class Universities: Destructive Pluralism by E. V. Balatsky, N. A. Ekimova

“…To test the formulated hypothesis, we used the previously developed algorithm for identifying WCU using statistical data from the fve Global University Rankings — Quacquarelli Symonds (QS), Times Higher Education (THE), Academic Ranking of World Universities (ARWU), Center for World University Rankings (CWUR) and National Taiwan University Ranking (NTU) — and two University Rankings by subject — QS and ARWU. …”
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The role of social determinants of health in the risk and prevention of group A streptococcal infection, acute rheumatic fever and rheumatic heart disease: A systematic review. by Pasqualina M Coffey, Anna P Ralph, Vicki L Krause

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DESIGN OF AN IMPROVED MODEL FOR CARDIOVASCULAR DISEASE DETECTION USING DEEP CANONICAL CORRELATION ANALYSIS AND BIOINSPIRED OPTIMIZATION by Prakash Chandra Sahoo, Binod Kumar Pattanayak, Rajani Kanta Mohanty, Ayasa Kanta Mohanty

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Differential Diagnostic Indicators for the Appointment of Non-Steroidal Anti-Inflammatory Therapy in the Treatment of Red Eye Syndrome at the Stage of Primary Outpatient Admission.... by V. N. Trubilin, E. G. Polunina, A. A. Kozhukhov, V. V. Kurenkov, N. V. Moreva, A. V. Trubilin, K. V. Chinenova

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A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS):... by Yuen Zhong, Yu Bao, Hong Cheng, Chaoqun Liu, Shengzhu Huang, Hualong Qiu, Honglin Huang, Jiajun Ren, Hailiu Jin, Caitong He, Long Tian, Yu Zhang, Bangzhu Luo, Tao Liang, Mujun Li, Zengnan Mo, Longman Li, Xiaobo Yang

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How do generative artificial intelligence (AI) tools and large language models (LLMs) influence language learners’ critical thinking in EFL education? A systematic review by Jing Liu, Ahmad Johari Bin Sihes, Ye Lu

“…The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. …”
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Predicting adolescent psychopathology from early life factors: A machine learning tutorial by Faizaan Siddique, Brian K. Lee

“…The best performing elastic net models included prenatal and family history factors (Sensitivity 0.654, Specificity 0.713; AUC 0.742, F1-score 0.401) and prenatal, family, history, and sociodemographic factors (Sensitivity 0.668, Specificity 0.704; AUC 0.745, F1-score 0.402). Across all 5 ML algorithms, family history factors (e.g., either parent had nervous breakdowns, trouble holding jobs/fights/police encounters, and counseling for mental issues) and sociodemographic covariates (e.g., maternal age, child's sex, caregiver income and caregiver education) tended to be better predictors of adolescent psychopathology. …”
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