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.

翻译：当前有证据表明，多种因素可能增加新生儿在出生时需接受稳定处理或复苏操作的风险。然而，这些风险因素尚未被完全认识，目前也没有通用的预测模型可用于识别高危状况。鉴于这些局限性，加之新生儿出生时需复苏的情况较为罕见，因此对负责产房新生儿护理的医护人员进行定期培训便显得尤为必要。本文提出了一种基于机器学习的方法，用于从真实数据中识别风险因素及其对出生事件的影响，医护人员可借此逐步提升并更新其知识储备。我们的最终目标是设计一款用户友好的移动应用程序，以提高高危患者的识别率并优化相应干预措施的规划。