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

Andrea Seveso; Valentina Bozzetti; Paolo Tagliabue; Maria Luisa Ventura; Federico Cabitza

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Paper

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

Topics: Big Data in Healthcare; Data Mining and Data Analysis; Decision Support Systems; Pattern Recognition and Machine Learning

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: BIOSTEC, 490-497, 2020 , Valletta, Malta

Authors: Andrea Seveso ¹ ; Valentina Bozzetti ² ; Paolo Tagliabue ² ; Maria Luisa Ventura ² and Federico Cabitza ¹

Affiliations: ¹ Department of Computer Sciences, Systems and Communications, University of Milano-Bicocca, Milan, Italy ; ² Neonatal Intensive Care Unit, MBBM Foundation, San Gerardo Hospital, Monza, Italy

Keyword(s): Machine Learning, Neonatal, Clinical Decision Support Systems, Data Science.

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).

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Paper citation in several formats:

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 (BIOSTEC 2020) - HEALTHINF; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 490-497. DOI: 10.5220/0008972804900497

@conference{healthinf20,
author={Andrea Seveso. and Valentina Bozzetti. and Paolo Tagliabue. and Maria Luisa 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 (BIOSTEC 2020) - HEALTHINF},
year={2020},
pages={490-497},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008972804900497},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF
TI - Developing a Machine Learning Model for Predicting Postnatal Growth in Very Low Birth Weight Infants
SN - 978-989-758-398-8
IS - 2184-4305
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
PB - SciTePress