Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

Abstract Background Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of tra...

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Bibliographic Details
Main Authors: Hoyt Burdick, Eduardo Pino, Denise Gabel-Comeau, Carol Gu, Jonathan Roberts, Sidney Le, Joseph Slote, Nicholas Saber, Emily Pellegrini, Abigail Green-Saxena, Jana Hoffman, Ritankar Das
Format: Article
Language:English
Published: BMC 2020-10-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Machine learning algorithm
Sepsis prediction
Severe sepsis
Diagnostic
Online Access:http://link.springer.com/article/10.1186/s12911-020-01284-x

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http://link.springer.com/article/10.1186/s12911-020-01284-x

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