TY - GEN
T1 - Towards an educational tool for supporting neonatologists in the delivery room
AU - Leonardi, Giorgio
AU - Maldarizzi, Clara
AU - Montani, Stefania
AU - Striani, Manuel
AU - Strozzi, Mariachiara
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/4/12
Y1 - 2024/4/12
N2 - Nowadays, there is evidence that several factors may increase the risk, for an infant, to require stabilisation or resuscitation manoeuvres at birth. However, this risk factors are not completely known, and a universally applicable model for predicting high-risk situations is not available yet. Considering both these limitations and the fact that the need for resuscitation at birth is a rare event, periodic training of the healthcare personnel responsible for newborn caring in the delivery room is mandatory. In this paper, we propose a machine learning approach for identifying risk factors and their impact on the birth event from real data, which can be used by personnel to progressively increase and update their knowledge. Our final goal will be the one of designing a user-friendly mobile application, able to improve the recognition rate and the planning of the appropriate interventions on high-risk patients.
AB - Nowadays, there is evidence that several factors may increase the risk, for an infant, to require stabilisation or resuscitation manoeuvres at birth. However, this risk factors are not completely known, and a universally applicable model for predicting high-risk situations is not available yet. Considering both these limitations and the fact that the need for resuscitation at birth is a rare event, periodic training of the healthcare personnel responsible for newborn caring in the delivery room is mandatory. In this paper, we propose a machine learning approach for identifying risk factors and their impact on the birth event from real data, which can be used by personnel to progressively increase and update their knowledge. Our final goal will be the one of designing a user-friendly mobile application, able to improve the recognition rate and the planning of the appropriate interventions on high-risk patients.
KW - Machine learning
KW - Risk factors
KW - neonatal resuscitation
KW - newborn babies
KW - risk prediction
UR - http://www.scopus.com/inward/record.url?scp=85203872348&partnerID=8YFLogxK
U2 - 10.1145/3664934.3664937
DO - 10.1145/3664934.3664937
M3 - Conference contribution
AN - SCOPUS:85203872348
T3 - ACM International Conference Proceeding Series
SP - 31
EP - 36
BT - Proceedings of the 9th International Conference on Information and Education Innovations, ICIEI 2024
PB - Association for Computing Machinery
T2 - 9th International Conference on Information and Education Innovations, ICIEI 2024
Y2 - 12 April 2024 through 14 April 2024
ER -