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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

Crossref DOI link: https://doi.org/10.1186/s12911-020-01284-x

Published Online: 2020-10-27

Published Print: 2020-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Burdick, Hoyt

Pino, Eduardo

Gabel-Comeau, Denise

Gu, Carol

Roberts, Jonathan

Le, Sidney

Slote, Joseph

Saber, Nicholas

Pellegrini, Emily

Green-Saxena, Abigail http://orcid.org/0000-0002-8502-6589
Hoffman, Jana

Das, Ritankar
Funding

Funding for this research was provided by:

National Center for Advancing Translational Sciences (1R43TR002309, 1R43TR002221)
License Information

Text and Data Mining valid from 2020-10-27

Version of Record valid from 2020-10-27
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Article History

Received: 16 December 2019

Accepted: 8 October 2020

First Online: 27 October 2020

Institutional Review Board (IRB) approval

: Not applicable.

: In compliance with the Health Insurance Portability and Accountability Act (HIPAA), all patient information was de-identified prior to retrospective analysis. All data collection was passive and did not have an impact on patient safety.

: Not applicable.

: All authors who have affiliations listed with Dascena (San Francisco, California, USA) are employees or contractors of Dascena.

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