{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:25Z","timestamp":1747216165445,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684567"},{"type":"electronic","value":"9781643684574"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>Outcome prediction is essential for the administration and treatment of critically ill patients. For those patients, clinical measurements are continuously monitored and the time-varying data contains rich information for assessing the patients\u2019 status. However, it is unclear how to capture the dynamic information effectively. In this work, multiple feature extraction methods, i.e. statistical feature classification methods and temporal modeling methods, such as recurrent neural network (RNN), were analyzed on a critical illness dataset with 18415 cases. The experimental results show when the dimension increases from 10 to 50, the RNN algorithm is gradually superior to the statistical feature classification methods with simple logic. The RNN model achieves the largest AUC value of 0.8463. Therefore, the temporal modeling methods are promising to capture temporal features which are predictive of the patients\u2019 outcome and can be extended in more clinical applications.<\/jats:p>","DOI":"10.3233\/shti231081","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:22Z","timestamp":1706178322000},"source":"Crossref","is-referenced-by-count":0,"title":["Extracting Dynamic Information of Temporal Clinical Data to Predict the Outcome in Critically Ill Patients"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5476-727X","authenticated-orcid":false,"given":"Jing","family":"Xia","sequence":"first","affiliation":[{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Ren","sequence":"additional","affiliation":[{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenchuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianshu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingsong","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China"},{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231081","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:23Z","timestamp":1706178323000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231081"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231081","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}