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Second, a context-aware dynamic reasoning mechanism is designed to simulate clinical situational awareness by adaptively modulating rule weights and decision thresholds based on individual medical history. Comparative experiments demonstrate that SH-BRB outperforms state-of-the-art models, such as XGBoost and CatBoost, under identical conditions. Specifically, the framework achieves a 99.3% precision in identifying high-risk individuals. Furthermore, randomized sampling experiments validate the model\u2019s transparency and decision traceability, ensuring clinical logic consistency. The proposed SH-BRB framework effectively integrates statistical rigor with transparent reasoning. It overcomes the limitations of \"black-box\" models and subjective expert systems, providing a trustworthy and verifiable decision-support tool for dynamic mental health assessment.<\/jats:p>","DOI":"10.1007\/s44443-026-00677-8","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T20:15:46Z","timestamp":1774383346000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SH-BRB: A semantically-grounded and explainable AI framework for dynamic mental health assessment"],"prefix":"10.1007","volume":"38","author":[{"given":"Yuhang","family":"Chi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinzhu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyi","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinrong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"677_CR1","doi-asserted-by":"publisher","first-page":"5810","DOI":"10.3390\/app11135810","volume":"11","author":"F Ahmed","year":"2021","unstructured":"Ahmed F, Hossain MS, Islam RU, Andersson K (2021) An evolutionary belief rule-based clinical decision support system to predict COVID-19 severity under uncertainty. 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