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Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study

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Title: Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study
Authors: Younga Kim, Hyeongsub Kim, Jaewoo Choi, Kyungjae Cho, Dongjoon Yoo, Yeha Lee, Su Jeong Park, Mun Hui Jeong, Seong Hee Jeong, Kyung Hee Park, Shin-Yun Byun, Taehwa Kim, Sung-Ho Ahn, Woo Hyun Cho, Narae Lee
Source: BMC Pediatrics, Vol 23, Iss 1, Pp 1-12 (2023)
Publisher Information: BMC, 2023.
Publication Year: 2023
Collection: LCC:Pediatrics
Subject Terms: Deep learning, Neonatal intensive care, Newborn, Respiratory failure, Intubation, Pediatrics, RJ1-570
More Details: Abstract Background Respiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician’s ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR). Methods We utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers. Results A mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1471-2431
Relation: https://doaj.org/toc/1471-2431
DOI: 10.1186/s12887-023-04350-1
Access URL: https://doaj.org/article/b2ecd22efde14942a0c413ca444c433b
Accession Number: edsdoj.b2ecd22efde14942a0c413ca444c433b
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  Data: Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study
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  Data: <searchLink fieldCode="AR" term="%22Younga+Kim%22">Younga Kim</searchLink><br /><searchLink fieldCode="AR" term="%22Hyeongsub+Kim%22">Hyeongsub Kim</searchLink><br /><searchLink fieldCode="AR" term="%22Jaewoo+Choi%22">Jaewoo Choi</searchLink><br /><searchLink fieldCode="AR" term="%22Kyungjae+Cho%22">Kyungjae Cho</searchLink><br /><searchLink fieldCode="AR" term="%22Dongjoon+Yoo%22">Dongjoon Yoo</searchLink><br /><searchLink fieldCode="AR" term="%22Yeha+Lee%22">Yeha Lee</searchLink><br /><searchLink fieldCode="AR" term="%22Su+Jeong+Park%22">Su Jeong Park</searchLink><br /><searchLink fieldCode="AR" term="%22Mun+Hui+Jeong%22">Mun Hui Jeong</searchLink><br /><searchLink fieldCode="AR" term="%22Seong+Hee+Jeong%22">Seong Hee Jeong</searchLink><br /><searchLink fieldCode="AR" term="%22Kyung+Hee+Park%22">Kyung Hee Park</searchLink><br /><searchLink fieldCode="AR" term="%22Shin-Yun+Byun%22">Shin-Yun Byun</searchLink><br /><searchLink fieldCode="AR" term="%22Taehwa+Kim%22">Taehwa Kim</searchLink><br /><searchLink fieldCode="AR" term="%22Sung-Ho+Ahn%22">Sung-Ho Ahn</searchLink><br /><searchLink fieldCode="AR" term="%22Woo+Hyun+Cho%22">Woo Hyun Cho</searchLink><br /><searchLink fieldCode="AR" term="%22Narae+Lee%22">Narae Lee</searchLink>
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  Data: BMC Pediatrics, Vol 23, Iss 1, Pp 1-12 (2023)
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  Data: Abstract Background Respiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician’s ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR). Methods We utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers. Results A mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P
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