Towards an educational tool for supporting neonatologists in the
  delivery room
Giorgio Leonardi Clara Maldarizzi Stefania Montani Manuel Striani Mariachiara Martina Strozzi
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.