An application of convolutional neural networks for the early detection of late-onset neonatal sepsis

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Preterm newborns are vulnerable and easily infected due to the immature immune system. Late-onset neonatal sepsis occurring 48 hours after birth is a widespread disease among preterm newborns leading to high mortality and morbidity rates. The diagnosis is primarily based on biochemistry test, and the prescribed treatment is to use antibiotics. Risk averse clinicians, often applied overdose to reduce the mortality. A non-invasive method on monitoring vital sign signals deterioration to predict late-onset neonatal sepsis is proposed in this paper. First, we set up collectors within the local networks in Neonatal Intensive Care Units (NICUs) where bedside monitoring machines locate to capture the necessary data. Then they were transformed to images according to specific rules. Finally, a convolutional neural network was built to predict the onset of sepsis. Pilot experiments conducted on data we have collected demonstrated the feasibility of this deep learning model. This method could be incorporated into the current clinical workflow as a decision support system and provide useful information for clinicians.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN) 2019
EditorsPlamen Angelov, Manuel Roveri
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728119854
ISBN (Print)9781728119861
DOIs
Publication statusPublished - 2019
EventIEEE International Joint Conference on Neural Networks 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019
https://www.ijcnn.org/

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2019
Abbreviated titleIJCNN 2019
CountryHungary
CityBudapest
Period14/07/1919/07/19
Internet address

Keywords

  • Convolutional Neural Network
  • Early Detection
  • Neonatal Sepsis

Cite this

Hu, Y., Lee, V. C. S., & Tan, K. (2019). An application of convolutional neural networks for the early detection of late-onset neonatal sepsis. In P. Angelov, & M. Roveri (Eds.), International Joint Conference on Neural Networks (IJCNN) 2019 [8851683] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2019.8851683
Hu, Yifei ; Lee, Vincent C.S. ; Tan, Kenneth. / An application of convolutional neural networks for the early detection of late-onset neonatal sepsis. International Joint Conference on Neural Networks (IJCNN) 2019. editor / Plamen Angelov ; Manuel Roveri. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019.
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abstract = "Preterm newborns are vulnerable and easily infected due to the immature immune system. Late-onset neonatal sepsis occurring 48 hours after birth is a widespread disease among preterm newborns leading to high mortality and morbidity rates. The diagnosis is primarily based on biochemistry test, and the prescribed treatment is to use antibiotics. Risk averse clinicians, often applied overdose to reduce the mortality. A non-invasive method on monitoring vital sign signals deterioration to predict late-onset neonatal sepsis is proposed in this paper. First, we set up collectors within the local networks in Neonatal Intensive Care Units (NICUs) where bedside monitoring machines locate to capture the necessary data. Then they were transformed to images according to specific rules. Finally, a convolutional neural network was built to predict the onset of sepsis. Pilot experiments conducted on data we have collected demonstrated the feasibility of this deep learning model. This method could be incorporated into the current clinical workflow as a decision support system and provide useful information for clinicians.",
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Hu, Y, Lee, VCS & Tan, K 2019, An application of convolutional neural networks for the early detection of late-onset neonatal sepsis. in P Angelov & M Roveri (eds), International Joint Conference on Neural Networks (IJCNN) 2019., 8851683, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, IEEE International Joint Conference on Neural Networks 2019, Budapest, Hungary, 14/07/19. https://doi.org/10.1109/IJCNN.2019.8851683

An application of convolutional neural networks for the early detection of late-onset neonatal sepsis. / Hu, Yifei; Lee, Vincent C.S.; Tan, Kenneth.

International Joint Conference on Neural Networks (IJCNN) 2019. ed. / Plamen Angelov; Manuel Roveri. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. 8851683.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - Preterm newborns are vulnerable and easily infected due to the immature immune system. Late-onset neonatal sepsis occurring 48 hours after birth is a widespread disease among preterm newborns leading to high mortality and morbidity rates. The diagnosis is primarily based on biochemistry test, and the prescribed treatment is to use antibiotics. Risk averse clinicians, often applied overdose to reduce the mortality. A non-invasive method on monitoring vital sign signals deterioration to predict late-onset neonatal sepsis is proposed in this paper. First, we set up collectors within the local networks in Neonatal Intensive Care Units (NICUs) where bedside monitoring machines locate to capture the necessary data. Then they were transformed to images according to specific rules. Finally, a convolutional neural network was built to predict the onset of sepsis. Pilot experiments conducted on data we have collected demonstrated the feasibility of this deep learning model. This method could be incorporated into the current clinical workflow as a decision support system and provide useful information for clinicians.

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Hu Y, Lee VCS, Tan K. An application of convolutional neural networks for the early detection of late-onset neonatal sepsis. In Angelov P, Roveri M, editors, International Joint Conference on Neural Networks (IJCNN) 2019. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. 8851683 https://doi.org/10.1109/IJCNN.2019.8851683