{"id":10177,"date":"2026-08-17T21:24:37","date_gmt":"2026-08-17T13:24:37","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10177"},"modified":"2026-08-17T23:55:00","modified_gmt":"2026-08-17T15:55:00","slug":"jase-202611-34-046","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-046","title":{"rendered":"A Rapid Mental Health Screening Algorithm for College Students Based on Elmo-CNN"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Jing Ma1<\/sup><\/i><\/a> and Yaming Hu2<\/sup><\/p>\n\n\n\n

1<\/sup>Department of Education, Faculty of Humanities and Arts, Xi\u2019an FanYi University, Xi\u2019an, 710000, China<\/p>\n\n\n\n

1,2<\/sup>Faculty of Science and Engineering, Xi\u2019an FanYi University, Xi\u2019an, 710000, China<\/p>\n<\/div>\n\n\n\n

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Received: May 18, 2026
Accepted: July 05, 2026
Publication Date: August 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall Performance Evaluation of the Proposed Elmo-CNN Model <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.046<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Mental health issues among university students are increasing and negatively affect academic performance and quality of life. Traditional clinical interviews and surveys are time-consuming, costly, and often delayed, limiting timely intervention. This study proposes a rapid mental health screening system using machine learning to classify students into low, moderate, and high-risk groups by processing both structured and unstructured data. The framework integrates ELMo contextual embeddings to capture semantic meaning from text and CNN to extract high-level features. Demographic, lifestyle, and behavioral data are also incorporated. The proposed ELMo-CNN model achieved strong performance with 96.82% accuracy, 96.14% precision, 95.47% recall, 95.80% F1-score, and 0.9821 ROC-AUC, outperforming baseline models such as SVM and CNN. The results indicate that the model is effective for fast and accurate mental health screening in real-world educational settings.<\/p>\n\n\n\n

Keywords: Mental Health Screening, ELMo Embeddings, College Students, Risk Classification, Deep Learning<\/em><\/p>\n\n\n\n

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