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 language | English |
|---|---|
| Title of host publication | International Joint Conference on Neural Networks (IJCNN) 2019 |
| Editors | Plamen Angelov, Manuel Roveri |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Number of pages | 8 |
| ISBN (Electronic) | 9781728119854 |
| ISBN (Print) | 9781728119861 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | IEEE International Joint Conference on Neural Networks 2019 - Budapest, Hungary Duration: 14 Jul 2019 → 19 Jul 2019 https://www.ijcnn.org/ |
Conference
| Conference | IEEE International Joint Conference on Neural Networks 2019 |
|---|---|
| Abbreviated title | IJCNN 2019 |
| Country | Hungary |
| City | Budapest |
| Period | 14/07/19 → 19/07/19 |
| Internet address |
Keywords
- Convolutional Neural Network
- Early Detection
- Neonatal Sepsis
Cite this
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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 proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - An application of convolutional neural networks for the early detection of late-onset neonatal sepsis
AU - Hu, Yifei
AU - Lee, Vincent C.S.
AU - Tan, Kenneth
PY - 2019
Y1 - 2019
N2 - 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.
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.
KW - Convolutional Neural Network
KW - Early Detection
KW - Neonatal Sepsis
UR - http://www.scopus.com/inward/record.url?scp=85073234907&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2019.8851683
DO - 10.1109/IJCNN.2019.8851683
M3 - Conference Paper
SN - 9781728119861
BT - International Joint Conference on Neural Networks (IJCNN) 2019
A2 - Angelov, Plamen
A2 - Roveri, Manuel
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway NJ USA
ER -