Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction

TL;DR

This paper introduces a novel approach for early sepsis prediction by leveraging implicit expert knowledge through physician questionnaires, avoiding circularity in label definition, and achieving state-of-the-art results.

Contribution

It proposes an independent ground truth for sepsis prediction using physician judgments, enhancing interpretability and validity of machine learning models.

Findings

Achieved state-of-the-art AUROC scores with small dataset

Identified surprising feature contributions through model interpretation

Provided a non-circular, expert-informed approach to sepsis prediction

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.