{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T14:08:45Z","timestamp":1723039725334},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643682501"},{"type":"electronic","value":"9781643682518"}],"license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"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":[[2022,1,14]]},"abstract":"<jats:p>Critical care can benefit from analyzing data by machine learning approaches for supporting clinical routine and guiding clinical decision-making. Developing data-driven approaches for an early detection of systemic inflammatory response syndrome (SIRS) in patients of pediatric intensive care and exploring the possibility of an approach using training data sets labeled automatically beforehand by knowledge-based approaches rather than clinical experts. Using na\u00efve Bayes classifier and an artificial neuronal network (ANN), trained with real data labeled by (1) domain experts ad (2) a knowledge-based decision support system (CDSS). Accuracies were evaluated by the data set labeled by domain experts using a 10-fold cross validation. The ANN approach trained with data labeled by domain experts yielded a specificity of 0.9139 and sensitivity of 0.8979, whereas the approach trained with a data set labeled by a knowledge-based CDSS achieves a specificity of 0.9220 and a sensitivity of 0.8887. ANN yielded promising results for data-driven detection of pediatric SIRS with real data. Our comparison shows the feasibility of using training data labeled automatically by knowledge-based approaches rather than manually allocated by experts.<\/jats:p>","DOI":"10.3233\/shti210901","type":"book-chapter","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T10:53:42Z","timestamp":1642416822000},"source":"Crossref","is-referenced-by-count":0,"title":["Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data"],"prefix":"10.3233","author":[{"given":"Marcel","family":"Mast","sequence":"first","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Elise Study Group BodeLouisac MastMarcelc WulffAntjec MarschollekMichaelc SchamerSvend RathertHenningd JackThomasd BeerbaumPhilippd R\u00fcbsamenNicolee B\u00f6hnkeJuliae KarchAndr\u00e9e ProsunPronayaf WieseLenaf Groszewski-AndersChristiang HallerAndreasg SlawikMathiasg FrankTorsteng c Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany d Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Hannover, Germany e Institut","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Marschollek","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Jack","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antje","family":"Wulff","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Informatics and Technology in Clinical Care and Public Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210901","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T10:53:43Z","timestamp":1642416823000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"ISBN":["9781643682501","9781643682518"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210901","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2022,1,14]]}}}