Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”

Abstract Sepsis is a leading cause of morbidity and mortality worldwide. Early identification of sepsis is important as it allows timely administration of potentially life-saving resuscitation and antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a de...

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Autores principales:	Supreeth P. Shashikumar, Gabriel Wardi, Atul Malhotra, Shamim Nemati
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/6852a8b842cb4d289b750afc458515bb
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spelling	oai:doaj.org-article:6852a8b842cb4d289b750afc458515bb2021-12-02T17:41:16ZArtificial intelligence sepsis prediction algorithm learns to say “I don’t know”10.1038/s41746-021-00504-62398-6352https://doaj.org/article/6852a8b842cb4d289b750afc458515bb2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00504-6https://doaj.org/toc/2398-6352Abstract Sepsis is a leading cause of morbidity and mortality worldwide. Early identification of sepsis is important as it allows timely administration of potentially life-saving resuscitation and antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model for the early prediction of sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift and data drifts. COMPOSER flags these unfamiliar cases as indeterminate rather than making spurious predictions. Six patient cohorts (515,720 patients) curated from two healthcare systems in the United States across intensive care units (ICU) and emergency departments (ED) were used to train and externally and temporally validate this model. In a sequential prediction setting, COMPOSER achieved a consistently high area under the curve (AUC) (ICU: 0.925–0.953; ED: 0.938–0.945). Out of over 6 million prediction windows roughly 20% and 8% were identified as indeterminate amongst non-septic and septic patients, respectively. COMPOSER provided early warning within a clinically actionable timeframe (ICU: 12.2 [3.2 22.8] and ED: 2.1 [0.8 4.5] hours prior to first antibiotics order) across all six cohorts, thus allowing for identification and prioritization of patients at high risk for sepsis.Supreeth P. ShashikumarGabriel WardiAtul MalhotraShamim NematiNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Supreeth P. Shashikumar Gabriel Wardi Atul Malhotra Shamim Nemati Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
description	Abstract Sepsis is a leading cause of morbidity and mortality worldwide. Early identification of sepsis is important as it allows timely administration of potentially life-saving resuscitation and antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model for the early prediction of sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift and data drifts. COMPOSER flags these unfamiliar cases as indeterminate rather than making spurious predictions. Six patient cohorts (515,720 patients) curated from two healthcare systems in the United States across intensive care units (ICU) and emergency departments (ED) were used to train and externally and temporally validate this model. In a sequential prediction setting, COMPOSER achieved a consistently high area under the curve (AUC) (ICU: 0.925–0.953; ED: 0.938–0.945). Out of over 6 million prediction windows roughly 20% and 8% were identified as indeterminate amongst non-septic and septic patients, respectively. COMPOSER provided early warning within a clinically actionable timeframe (ICU: 12.2 [3.2 22.8] and ED: 2.1 [0.8 4.5] hours prior to first antibiotics order) across all six cohorts, thus allowing for identification and prioritization of patients at high risk for sepsis.
format	article
author	Supreeth P. Shashikumar Gabriel Wardi Atul Malhotra Shamim Nemati
author_facet	Supreeth P. Shashikumar Gabriel Wardi Atul Malhotra Shamim Nemati
author_sort	Supreeth P. Shashikumar
title	Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
title_short	Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
title_full	Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
title_fullStr	Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
title_full_unstemmed	Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
title_sort	artificial intelligence sepsis prediction algorithm learns to say “i don’t know”
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6852a8b842cb4d289b750afc458515bb
work_keys_str_mv	AT supreethpshashikumar artificialintelligencesepsispredictionalgorithmlearnstosayidontknow AT gabrielwardi artificialintelligencesepsispredictionalgorithmlearnstosayidontknow AT atulmalhotra artificialintelligencesepsispredictionalgorithmlearnstosayidontknow AT shamimnemati artificialintelligencesepsispredictionalgorithmlearnstosayidontknow
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Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”

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