{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T04:41:05Z","timestamp":1793940065646,"version":"4.8.0"},"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":[[2020,7]]},"abstract":"<jats:p>The prediction of the risk profile related to the cardiopathy complication is a core research task that could support clinical decision making. However, the design and implementation of a clinical decision support system based on Electronic Health Record (EHR) temporal data comprise of several challenges. Several single task learning approaches consider the prediction of the risk profile related to a specific diabetes complication (i.e., cardiopathy) independent from other complications. Accordingly, the state-of-the-art multi-task learning (MTL) model encapsulates only the temporal relatedness among the EHR data. However, this assumption might be restricted in the clinical scenario where both spatio-temporal constraints should be taken into account. The aim of this study is the proposal of two different MTL procedures, called spatio-temporal lasso (STL-MTL) and spatio-temporal group lasso (STGL-MTL), which encode the spatio-temporal relatedness using a regularization term and a graph-based approach (i.e., encoding the task relatedness using the structure matrix). Experimental results on a real-world EHR dataset demonstrate the robust performance and the interpretability of the proposed approach.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/593","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"4299-4305","source":"Crossref","is-referenced-by-count":9,"title":["A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study"],"prefix":"10.24963","author":[{"given":"Luca","family":"Romeo","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Armentano","sequence":"additional","affiliation":[{"name":"Diabetological Center DEA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Nicolucci","sequence":"additional","affiliation":[{"name":"Center for Outcomes Research and Clinical Epidemiology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Vespasiani","sequence":"additional","affiliation":[{"name":"METEDA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Vespasiani","sequence":"additional","affiliation":[{"name":"METEDA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:16:10Z","timestamp":1594260970000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/593"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/593","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}