Abstract

Mortality scores based on multiple regressions are common in critical care medicine for prognostic stratification of patients. However, to be used at the point of care, they need to be both accurate and easily interpretable. In this work, we propose the application of one existent type of rule base system using statistical information — Probabilistic Fuzzy Systems (PFS) — to predict mortality of septic shock patients. To assess its accuracy and interpretability, these models are compared to methodologies previously proposed in this domain: Takagi-Sugeno fuzzy models and logistic regression models. The methods are tested using a retrospective cohort study including ICU patients with abdominal septic shock. Regarding accuracy, PFS models are comparable to fuzzy modeling and logistic regression. In terms of interpretability, results indicate that PFS models increase the transparency of the learned system (using fuzzy rules), but at the same time, provide additional means for validating the fuzzy classifier using expert knowledge (from physicians in this paper). By providing accurate and interpretable estimates for the mortality risk, results suggest the usefulness of PFS to develop scores for critical care medicine.

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