Machine learning for early detection of sepsis: an internal and temporal validation study.

ABSTRACT:

Objective

Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

Materials and methods

We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1,

SUBMITTER: Bedoya AD 

PROVIDER: S-EPMC7382639 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature