Prediction of clinicians' treatment in preterm infants with suspected late-onset sepsis - an ML approach

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Abstract

As a prevalent disease of preterm infants, late-onset neonatal sepsis has taken up a huge proportion of morbidity and mortality of newborn babies. We have been continuously capturing vital signs of preterm infants in NICU, and proposed a non-invasive method based on machine learning techniques to predict the clinicians' treatment on them. Then we provide evaluation of predictive models and prove their feasibility. Our models could help the pediatricians make wiser clinical decision, such as more accurate treatment, avoiding the abuse of antibiotics to some extent.

Original languageEnglish
Title of host publicationProceedings of The 13th IEEE Conference on Industrial Electronics and Applications (ICIEA 2018)
Subtitle of host publication31 May – 2 June 2018 Wuhan, China
EditorsLijun Jiang , Liangcai Zeng
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1177-1182
Number of pages6
ISBN (Electronic)9781538637586, 9781538637579
ISBN (Print)9781538637593
DOIs
Publication statusPublished - 28 Jun 2018
EventIEEE Conference on Industrial Electronics and Applications 2018 - Wuhan, China
Duration: 31 May 20182 Jun 2018
Conference number: 13th
http://www.ieeeiciea.org/2018/

Conference

ConferenceIEEE Conference on Industrial Electronics and Applications 2018
Abbreviated titleICIEA 2018
CountryChina
CityWuhan
Period31/05/182/06/18
Internet address

Keywords

  • machine learning
  • neonatal sepsis
  • prediction
  • vital signs (key words)

Cite this

Hu, Y., .C.S. Lee, V., & Tan, K. (2018). Prediction of clinicians' treatment in preterm infants with suspected late-onset sepsis - an ML approach. In L. Jiang , & L. Zeng (Eds.), Proceedings of The 13th IEEE Conference on Industrial Electronics and Applications (ICIEA 2018): 31 May – 2 June 2018 Wuhan, China (pp. 1177-1182). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIEA.2018.8397888