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Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction
استفاده از دانش تخصصی ضمنی برای یادگیری ماشین غیر دایره ای در پیش بینی سپسیس-2019 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. Keywords: Machine learning in health care | Sepsis prediction |
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