{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T12:41:42Z","timestamp":1753879302912,"version":"3.41.2"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004694","name":"Kaohsiung Medical University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004694","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Mycoses"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Clinical severity scores, such as acute physiology, age, chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), Pitt Bacteremia Score (PBS), and European Confederation of Medical Mycology Quality (EQUAL) score, may not reliably predict candidemia prognosis owing to their prespecified scorings that can limit their adaptability and applicability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>Unlike those fixed and prespecified scorings, we aim to develop and validate a machine learning (ML) approach that is able to learn predictive models adaptively from available patient data to increase adaptability and applicability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Different ML algorithms follow different design philosophies and consequently, they carry different learning biases. We have designed an ensemble meta\u2010learner based on stacked generalisation to integrate multiple learners as a team to work at its best in a synergy to improve predictive performances.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the multicenter retrospective study, we analysed 512 patients with candidemia from January 2014 to July 2019 and compared a stacked generalisation model (SGM) with APACHE II, SOFA, PBS and EQUAL score to predict the 14\u2010day mortality. The cross\u2010validation results showed that the SGM significantly outperformed APACHE II, SOFA, PBS, and EQUAL score across several metrics, including F1\u2010score (0.68, <jats:italic>p<\/jats:italic>\u2009&lt;\u2009.005), Matthews correlation coefficient (0.54, <jats:italic>p<\/jats:italic>\u2009&lt;\u2009.05 vs. SOFA, <jats:italic>p<\/jats:italic>\u2009&lt;\u2009.005 vs. the others) and the area under the curve (AUC; 0.87, <jats:italic>p<\/jats:italic>\u2009&lt;\u2009.005). In addition, in an independent external test, the model effectively predicted patients' mortality in the external validation cohort, with an AUC of 0.77.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>ML models show potential for improving mortality prediction amongst patients with candidemia compared to clinical severity scores.<\/jats:p><\/jats:sec>","DOI":"10.1111\/myc.13667","type":"journal-article","created":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T03:00:54Z","timestamp":1698894054000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and comparison with clinical severity scores"],"prefix":"10.1111","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1423-4647","authenticated-orcid":false,"given":"Wei\u2010Huan","family":"Hu","sequence":"first","affiliation":[{"name":"College of Computer Science, National Yang Ming Chiao Tung University  Hsinchu Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-4077","authenticated-orcid":false,"given":"Shang\u2010Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Internal Medicine Kaohsiung Medical University Hospital, Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"College of Medicine, Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Department of Laboratory Medicine Kaohsiung Medical University Hospital  Kaohsiung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuh\u2010Jyh","family":"Hu","sequence":"additional","affiliation":[{"name":"College 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