{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:46:29Z","timestamp":1773380789430,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>In education, stress, anxiety, and depression are developing issues that impair student performance and well-being. Slow and unscalable surveys and manual counseling dominate existing methods. This research introduces PsyCrisCare, a machine learning-based paradigm for student psychological crisis risk risk prediction and hierarchical intervention. The goal is to identify dangers early, classify kids by well-being, and propose support.GPA, sleep hours, daily steps, emotional ratings (stress, anxiety, depression), and self-reported text inputs (daily reflections) are integrated by PsyCrisCare. A hybrid pipeline uses feature engineering, sentiment analysis, and hierarchical classification to classify kids as Healthy, At-risk, or Struggling. In experiments, PsyCrisCare outperformed baseline classifiers by 8\u201312% with an accuracy of 91.3%, F1-score of 0.89, and AUROC of 0.92. The classifier initially divides all the students into two groups: those who are healthy and those who are not. On a public dataset of 500 students, PsyCrisCare fares better than baseline classifiers like Random Forest and XGBoost. The approach improves the Struggling group recall from 0.71 to 0.86, detecting at-risk students early. Analysis of 5-fold cross-validation reveals stable performance with limited volatility (\u00b11.2%), demonstrating fairness and robustness across subgroups. In conclusion, PsyCrisCare has excellent potential as an AI-powered platform for proactive mental health monitoring, early crisis risk risk prediction, and targeted educational intervention.<\/jats:p>","DOI":"10.31449\/inf.v50i9.11328","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:18Z","timestamp":1773354078000},"source":"Crossref","is-referenced-by-count":0,"title":["PsyCrisCare: A Multimodal BERT\u2013DNN Fusion with Hierarchical Classification for Student Psychological crisis risk Risk Prediction"],"prefix":"10.31449","volume":"50","author":[{"given":"Jing","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,12]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11328\/6573","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11328\/6573","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:19Z","timestamp":1773354079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/11328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i9.11328","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}