Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

Abstract Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We p...

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Autores principales:	Jacob Deasy, Pietro Liò, Ari Ercole
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/967a5528348445e399c446557e5db6f7
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spelling	oai:doaj.org-article:967a5528348445e399c446557e5db6f72021-12-02T13:58:15ZDynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation10.1038/s41598-020-79142-z2045-2322https://doaj.org/article/967a5528348445e399c446557e5db6f72020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79142-zhttps://doaj.org/toc/2045-2322Abstract Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient’s stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83–0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.Jacob DeasyPietro LiòAri ErcoleNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jacob Deasy Pietro Liò Ari Ercole Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
description	Abstract Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient’s stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83–0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.
format	article
author	Jacob Deasy Pietro Liò Ari Ercole
author_facet	Jacob Deasy Pietro Liò Ari Ercole
author_sort	Jacob Deasy
title	Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
title_short	Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
title_full	Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
title_fullStr	Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
title_full_unstemmed	Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
title_sort	dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/967a5528348445e399c446557e5db6f7
work_keys_str_mv	AT jacobdeasy dynamicsurvivalpredictioninintensivecareunitsfromheterogeneoustimeserieswithouttheneedforvariableselectionorcuration AT pietrolio dynamicsurvivalpredictioninintensivecareunitsfromheterogeneoustimeserieswithouttheneedforvariableselectionorcuration AT ariercole dynamicsurvivalpredictioninintensivecareunitsfromheterogeneoustimeserieswithouttheneedforvariableselectionorcuration
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Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

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