Gemelo digital como herramienta para la prevención del síndrome de muerte súbita infantil

Abstract

In the technology and healthcare field, multiple healthcare technologies have emerged. However, digital twin technology is being explored in a limited way due to the difficulties in creating a digital replica of a person. On the other hand, in healthcare, activities such as taking vital signs have in some cases become a routine task that is not given the importance it really deserves. Its correct recording and analysis allow physicians to identify possible scenarios that represent a danger to the patient, especially in patients under one year of age, which can trigger sudden infant death syndrome. In this context, this degree work proposes the development of a digital twin prototype that serves as a tool that allows parents and health personnel to reduce the probability of sudden infant death syndrome. The prototype will include components for monitoring vital signs, visualization of alerts generated from the analysis of vital signs using a Machine Learning model, an informative control panel and a simulator that will allow the analysis of vital signs using the Machine Learning model.