Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data

Abstract Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some i...

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Autores principales:	Jiarui Feng, Jennifer Lee, Zachary A. Vesoulis, Fuhai Li
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/f6e5f1a2356849fd9a78f78113584b38
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spelling	oai:doaj.org-article:f6e5f1a2356849fd9a78f78113584b382021-12-02T18:30:56ZPredicting mortality risk for preterm infants using deep learning models with time-series vital sign data10.1038/s41746-021-00479-42398-6352https://doaj.org/article/f6e5f1a2356849fd9a78f78113584b382021-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00479-4https://doaj.org/toc/2398-6352Abstract Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization.Jiarui FengJennifer LeeZachary A. VesoulisFuhai LiNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-8 (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 Jiarui Feng Jennifer Lee Zachary A. Vesoulis Fuhai Li Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
description	Abstract Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization.
format	article
author	Jiarui Feng Jennifer Lee Zachary A. Vesoulis Fuhai Li
author_facet	Jiarui Feng Jennifer Lee Zachary A. Vesoulis Fuhai Li
author_sort	Jiarui Feng
title	Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
title_short	Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
title_full	Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
title_fullStr	Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
title_full_unstemmed	Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
title_sort	predicting mortality risk for preterm infants using deep learning models with time-series vital sign data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f6e5f1a2356849fd9a78f78113584b38
work_keys_str_mv	AT jiaruifeng predictingmortalityriskforpreterminfantsusingdeeplearningmodelswithtimeseriesvitalsigndata AT jenniferlee predictingmortalityriskforpreterminfantsusingdeeplearningmodelswithtimeseriesvitalsigndata AT zacharyavesoulis predictingmortalityriskforpreterminfantsusingdeeplearningmodelswithtimeseriesvitalsigndata AT fuhaili predictingmortalityriskforpreterminfantsusingdeeplearningmodelswithtimeseriesvitalsigndata
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Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data

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