A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

ABSTRACT:

Objectives

Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.

Design

Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation.

Setting

Tertiary teaching hospital system in Philadelphia, PA.

Patients

All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184).

Interventions

A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of s

SUBMITTER: Giannini HM 

PROVIDER: S-EPMC8635476 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature