{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:46:24Z","timestamp":1773380784548,"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>Monitoring vital signs is extremely important in neurological intensive care, as even minor changes can indicate a significant decline in the patient's condition. The purpose of this work is to develop DL-PRWarnNeuro, a deep learning model intended to provide early, accurate alerts in neurological intensive care settings by leveraging patient signal data from the MIMIC-IV dataset. A convolutional layer is incorporated into the model's architecture to extract local patterns. This is followed by transformer-based attentive layers, designed to focus on crucial signal fluctuations. During training, Focal Loss is used to address class imbalance between typical, stable states and rare, critical events. The performance of DL-PRWarnNeuro is tested against baseline models, including LSTM classifiers and standard threshold-based monitoring. Among the most important indicators are the early detection rate, the False Alarm Rate (FAR), and the Area Under the Receiver Operating Characteristics curve (AUROC). Compared to the baselines, the results show a 14% increase in AUROC, a 22% decrease in FAR, and an 18% increase in the early detection rate. These findings indicate that there was an improvement in clinical reliability and support for decisions for neurological intensive care unit monitors.<\/jats:p>","DOI":"10.31449\/inf.v50i9.12162","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:09Z","timestamp":1773354069000},"source":"Crossref","is-referenced-by-count":0,"title":["DL-PRWarnNeuro: A Transformer-Based Deep Learning Framework for Multimodal Patient Deterioration Prediction in Neurological Intensive Care"],"prefix":"10.31449","volume":"50","author":[{"given":"Man","family":"Hua","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\/12162\/6572","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12162\/6572","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:09Z","timestamp":1773354069000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12162"}},"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.v50i9.12162","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]]}}}