Predicting mortality risk for preterm infants using random forest

Abstract Mortality is an unfortunately common outcome of extremely and very preterm birth. Existing clinical prediction models capture mortality risk at birth but fail to account for the remainder of the hospital course. Infants born < 32 weeks gestation with complete physiologic and clinical dat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jennifer Lee, Jinjin Cai, Fuhai Li, Zachary A. Vesoulis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5f2c133598bb4e5e884623873be2e4dc
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5f2c133598bb4e5e884623873be2e4dc
record_format	dspace
spelling	oai:doaj.org-article:5f2c133598bb4e5e884623873be2e4dc2021-12-02T14:25:26ZPredicting mortality risk for preterm infants using random forest10.1038/s41598-021-86748-42045-2322https://doaj.org/article/5f2c133598bb4e5e884623873be2e4dc2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86748-4https://doaj.org/toc/2045-2322Abstract Mortality is an unfortunately common outcome of extremely and very preterm birth. Existing clinical prediction models capture mortality risk at birth but fail to account for the remainder of the hospital course. Infants born < 32 weeks gestation with complete physiologic and clinical data were included in this retrospective study. Mortality risk was quantified by conventional means (clinical factors) using the CRIB-II score and the optimal logistic regression model. A random forest (RF) model was trained using a subset of the cohort, labeling data within 6 h of death as “worry.” The model was then tested using the remaining infants. A total of 275 infants were included in the study with a mean gestational age of 27 weeks, mean birth weight of 929 g, 49% female, and a mortality rate of 21%. The CRIB-II and logistic regression models had acceptable performance with sensitivities of 71% and 80% AUC scores of 0.78 and 0.84, respectively. The RF model had superior performance with a sensitivity of 88% and an AUC of 0.93. A random forest model which incorporates fixed clinical factors with the influence of aberrancies in subsequent physiology has superior performance for mortality prediction compared to conventional models.Jennifer LeeJinjin CaiFuhai LiZachary A. VesoulisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jennifer Lee Jinjin Cai Fuhai Li Zachary A. Vesoulis Predicting mortality risk for preterm infants using random forest
description	Abstract Mortality is an unfortunately common outcome of extremely and very preterm birth. Existing clinical prediction models capture mortality risk at birth but fail to account for the remainder of the hospital course. Infants born < 32 weeks gestation with complete physiologic and clinical data were included in this retrospective study. Mortality risk was quantified by conventional means (clinical factors) using the CRIB-II score and the optimal logistic regression model. A random forest (RF) model was trained using a subset of the cohort, labeling data within 6 h of death as “worry.” The model was then tested using the remaining infants. A total of 275 infants were included in the study with a mean gestational age of 27 weeks, mean birth weight of 929 g, 49% female, and a mortality rate of 21%. The CRIB-II and logistic regression models had acceptable performance with sensitivities of 71% and 80% AUC scores of 0.78 and 0.84, respectively. The RF model had superior performance with a sensitivity of 88% and an AUC of 0.93. A random forest model which incorporates fixed clinical factors with the influence of aberrancies in subsequent physiology has superior performance for mortality prediction compared to conventional models.
format	article
author	Jennifer Lee Jinjin Cai Fuhai Li Zachary A. Vesoulis
author_facet	Jennifer Lee Jinjin Cai Fuhai Li Zachary A. Vesoulis
author_sort	Jennifer Lee
title	Predicting mortality risk for preterm infants using random forest
title_short	Predicting mortality risk for preterm infants using random forest
title_full	Predicting mortality risk for preterm infants using random forest
title_fullStr	Predicting mortality risk for preterm infants using random forest
title_full_unstemmed	Predicting mortality risk for preterm infants using random forest
title_sort	predicting mortality risk for preterm infants using random forest
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5f2c133598bb4e5e884623873be2e4dc
work_keys_str_mv	AT jenniferlee predictingmortalityriskforpreterminfantsusingrandomforest AT jinjincai predictingmortalityriskforpreterminfantsusingrandomforest AT fuhaili predictingmortalityriskforpreterminfantsusingrandomforest AT zacharyavesoulis predictingmortalityriskforpreterminfantsusingrandomforest
_version_	1718391369988308992

Predicting mortality risk for preterm infants using random forest

Ejemplares similares