Making the Invisible Visible: Intelligent Recovery Monitoring of Aortic Arch Repair Surgery Proposal

• Autor(a) principal: Dios, Mercedes
• Data de Publicação: 2019
• Outros Autores: Mendes, David, Gomez-Cantarino, Sagrario, Sim-Sim, Margarida
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/25663; https://doi.org/de Dios M., Mendes D., Gomez-Cantarino S. & Sim-Sim M. (2019). Making the Invisible Visible: Intelligent Recovery Monitoring of Aortic Arch Repair Surgery Proposal. In: J. García-Alonso & C. Fonseca (Eds) Gerontechnology. IWoG 2018. Communications in Computer and Information Science, vol 1016. Springer, Cham. https://doi.org/10.1007/978-3-030-16028-9_16
• topic: aortic arch; Postsurgical complications; Perfusion; Nursing; Primary care; Continuous monitoring

Thoracic pain is a very frequent reason for consultation in the primary care nursing consultation. However, when the healthcare professional is facing a patient with intense and tearing pain in the chest that induces him to think that he is facing a possible aortic dissection, then it is in an emergency where the patient requires immediate attention and a referral without loss of time to a cardiac surgery unit. This study aims to publicize the misfortunes that may occur in the patient during the recovery of aortic arch repair surgery. The results were obtained through the analysis of the clinical history of patients with aortic pathology, all of them operated in the cardiac surgery unit of the Virgen de la Salud Hospital of Toledo (CHT) Spain. We are proposing a continuous monitoring solution that can ascertain the life quality of patients that went arch repair surgery. Life quality is difficult to measure quantitatively. We suggest threshold levels for a complex dataset that, when considered simultaneously through data fusion techniques applied with reinforcement learning algorithms can have a numeric output for quality of life as a whole. In this groundbreaking paper, the fundaments of the ontological structure for data acquisition, model definition, data acquisition and reasoning based in deep learning techniques are introduced.