Medical decision support using machine learning for early detection of late onset neonatal sepsis

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Creating Medical decision support using machine learning for early detection of late onset neonatal sepsis [1]

Abstract

The objective for the study was to develop models for late-onset for neonatal sepsis. This study used machine learning(ML) which is a subfield in the artificial intelligence. Earlier work showed feasibility of building predictive models with clinical potential.

Design

299 Infants admitted to neonatal intensive care unit in the Monroe Carell JR. Childrens hosptial at Vanderbilt Developed Nine Machine Learning (ML) algorithms

  • Study of 18 month period
  • Data

1826 total admission and sample size was 299.

Definition of late onset was defined as neonatal sepsis occurring over 72 hours after birth.

Measurement

Compared blood work

  1. sensitivity
  2. specificity
  3. Positive value
  4. negative value

Results

Treatment sensitivity

  • Top three predictive variables
  1. hemotocrit
  2. packed cell volume
  3. Chorioamnionitis and Respiratory reate


Conclusion

The study showed that with the use of the Electronic health record and the machine learning(ML) algorithms exceeds the sensitivity and specificity of clinicians.

Reference

  1. Mani, S., Ozdas, A., Aliferis, C., Varol, H. A., Chen, Q., Carnevale, R., ... & Weitkamp, J. H. (2014). Medical decision support using machine learning for early detection of late-onset neonatal sepsis. Journal of the American Medical Informatics Association, 21(2), 326-336.