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Description

Appropriately interpreting clinical decision support and predictive models is vital when caring for patients in an acute care setting. We implemented a state machine to aid in interpretability of machine learning based risk scores that provide automatic real-time risk assessment for patients with sepsis. This study prospectively compared the discriminatory capacity of two sepsis predictive models for early sepsis identification in the emergency department after implementation within a state machine

Learning Objective 1: Determine whether discretizing machine learning models into sepsis risk strata within state machine can aid in prediction.

Learning Objective 2: Compare the discriminatory capacity of two state machines after implementing different predictive models when classifying risk of sepsis.

Authors:

Samuel McDonald (Presenter)
UT Southwestern

Karen Wang, UT Southwestern
DuWayne Willett, UT Southwestern
Mujeeb Basit, UT Southwestern

Presentation Materials:

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