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Verfasst von:Schamoni, Shigehiko [VerfasserIn]   i
 Lindner, Holger A. [VerfasserIn]   i
 Schneider-Lindner, Verena [VerfasserIn]   i
 Thiel, Manfred [VerfasserIn]   i
 Riezler, Stefan [VerfasserIn]   i
Titel:Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction
Verf.angabe:Shigehiko Schamoni, Holger A. Lindner, Verena Schneider-Lindner, Manfred Thiel, Stefan Riezler
E-Jahr:2019
Jahr:24 September 2019
Umfang:9 S.
Fussnoten:Gesehen am 25.11.2019
Titel Quelle:Enthalten in: Artificial intelligence in medicine
Ort Quelle:Amsterdam [u.a.] : Elsevier Science, 1989
Jahr Quelle:2019
Band/Heft Quelle:100(2019) Artikel-Nummer 101725, 9 Seiten
ISSN Quelle:1873-2860
Abstract:Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians’ daily judgements of patients’ sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.
DOI:doi:10.1016/j.artmed.2019.101725
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1016/j.artmed.2019.101725
 Volltext: http://www.sciencedirect.com/science/article/pii/S0933365718305700
 DOI: https://doi.org/10.1016/j.artmed.2019.101725
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Machine learning in health care
 Sepsis prediction
K10plus-PPN:168333762X
Verknüpfungen:→ Zeitschrift

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