{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T22:28:28Z","timestamp":1780957708196,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Early detection of patient deterioration is crucial for reducing mortality rates. Heart rate data has shown promise in assessing patient health, and wearable devices offer a cost-effective solution for real-time monitoring. However, extracting meaningful insights from diverse heart rate data and handling missing values in wearable device data remain key challenges.  To address these challenges, we propose TARL, an innovative approach that models the structural relationships of representative subsequences, known as shapelets, in heart rate time series. TARL creates a shapelet-transition knowledge graph to model shapelet dynamics in heart rate time series, indicating illness progression and potential future changes. We further introduce a transition-aware knowledge embedding to reinforce relationships among shapelets and quantify the impact of missing values, enabling the formulation of comprehensive heart rate representations. These representations capture explanatory structures and predict future heart rate trends, aiding early illness detection. We collaborate with physicians and nurses to gather ICU patient heart rate data from wearables and diagnostic metrics to assess illness severity and evaluate deterioration. Experiments on real-world ICU data demonstrate that TARL achieves both high reliability and early detection. A case study further showcases TARL's explainable detection process, highlighting its potential as an AI-driven tool to assist clinicians in recognizing early signs of patient deterioration.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1097","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9871-9879","source":"Crossref","is-referenced-by-count":1,"title":["Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System"],"prefix":"10.24963","author":[{"given":"Lo Pang-Yun","family":"Ting","sequence":"first","affiliation":[{"name":"Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Pei","family":"Chen","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"An-Shan","family":"Liu","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Yin","family":"Yeh","sequence":"additional","affiliation":[{"name":"Dept. of Nursing, National Cheng Kung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po-Lin","family":"Chen","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, National Cheng Kung University Hospital, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun-Ta","family":"Chuang","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:07Z","timestamp":1758627367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1097"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1097","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}