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A deep learning model for real-time mortality prediction in critically ill children

Kim, Soo Yeon ; Kim, Saehoon ; Cho, Joongbum ; Kim, Young Suh ; Sol, In Suk ; Sung, Youngchul ; Cho, Inhyeok ; Park, Minseop ; Jang, Haerin ; Kim, Yoon Hee ; Kim, Kyung Won ; Sohn, Myung Hyun

Critical care (London, England), 2019-08, Vol.23 (1), p.279-279, Article 279 [Periódico revisado por pares]

England: BioMed Central Ltd

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Título:
A deep learning model for real-time mortality prediction in critically ill children
Autor: Kim, Soo Yeon ; Kim, Saehoon ; Cho, Joongbum ; Kim, Young Suh ; Sol, In Suk ; Sung, Youngchul ; Cho, Inhyeok ; Park, Minseop ; Jang, Haerin ; Kim, Yoon Hee ; Kim, Kyung Won ; Sohn, Myung Hyun
Assuntos: Adolescent ; Algorithms ; Area Under Curve ; Artificial neural networks ; Big Data ; Body weight ; Child ; Child health ; Child, Preschool ; Children ; Cohort Studies ; Critical Illness - mortality ; Critically ill children ; Data mining ; Deep Learning ; Female ; Humans ; Infant ; Intensive care units, pediatric ; Machine learning ; Male ; Mortality ; Mortality - trends ; Patient outcomes ; Pediatric intensive care ; Pediatrics ; Pediatrics - instrumentation ; Pediatrics - methods ; Pediatrics - standards ; Prognosis ; Republic of Korea ; Retrospective Studies ; Risk assessment ; Risk Assessment - methods ; ROC Curve ; South Korea
É parte de: Critical care (London, England), 2019-08, Vol.23 (1), p.279-279, Article 279
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
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Descrição: The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89-0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.
Editor: England: BioMed Central Ltd
Idioma: Inglês

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A deep learning model for real-time mortality prediction in critically ill children

Kim, Soo Yeon ; Kim, Saehoon ; Cho, Joongbum ; Kim, Young Suh ; Sol, In Suk ; Sung, Youngchul ; Cho, Inhyeok ; Park, Minseop ; Jang, Haerin ; Kim, Yoon Hee ; Kim, Kyung Won ; Sohn, Myung Hyun

England: BioMed Central Ltd

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