Developing a Machine Learning Model for Predicting Postnatal Growth in Very Low Birth Weight Infants

Andrea Seveso, Valentina Bozzetti, Paolo Tagliabue, Maria Ventura, Federico Cabitza

Abstract

Objective of the work is the development of prognostic machine learning models that predict qualitative and quantitative measures of postnatal growth in very low birth weight preterm infants. Observational retrospective data about 964 infants at risk are retrieved from “Fondazione Monza e Brianza per il bambino e la mamma“’s electronic medical record. Both prenatal (gestational, socioeconomic, etc.) and perinatal (nutritional, respiratory assistance, drug prescription and daily growth) data up to a week after birth are the features included. Model’s performances are compared to previous literature and human performance, showing a substantial improvement (in e.g., best regression MAE=0.49, best classification AUC=0.94).

Download


Paper Citation


in Harvard Style

Seveso A., Bozzetti V., Tagliabue P., Ventura M. and Cabitza F. (2020). Developing a Machine Learning Model for Predicting Postnatal Growth in Very Low Birth Weight Infants.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 490-497. DOI: 10.5220/0008972804900497


in Bibtex Style

@conference{healthinf20,
author={Andrea Seveso and Valentina Bozzetti and Paolo Tagliabue and Maria Ventura and Federico Cabitza},
title={Developing a Machine Learning Model for Predicting Postnatal Growth in Very Low Birth Weight Infants},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2020},
pages={490-497},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008972804900497},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Developing a Machine Learning Model for Predicting Postnatal Growth in Very Low Birth Weight Infants
SN - 978-989-758-398-8
AU - Seveso A.
AU - Bozzetti V.
AU - Tagliabue P.
AU - Ventura M.
AU - Cabitza F.
PY - 2020
SP - 490
EP - 497
DO - 10.5220/0008972804900497