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

  • Mercedes de Dios
  • David MendesEmail author
  • Sagrario G. Cantarino
  • Margarida Sim Sim
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1016)

Abstract

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.

Keywords

Aortic arch Postsurgical complications Perfusion Primary care Nursing Continuous monitoring Ontology Disease model Reinforcement learning Data fusion 

Notes

Acknowledgements

To our patients, source of inspiration and wisdom. The work done with them provides us with the necessary knowledge to reduce their suffering and improve the quality of nursing care.

This work was supported by 4IE project (0045-4IE-4-P) funded by the Interreg V-A España-Portugal (POCTEP) 2014-2020 program by the European Regional Development Fund.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Health Service of Castilla-La Mancha (SESCAM)Talavera de la Reina, ToledoSpain
  2. 2.ENDOCU GroupUniversity School of Nursing and Physiotherapy Toledo (Nursing, Pain and Care)ToledoSpain
  3. 3.School of Nursing S. João de DeusUniversity of EvoraÉvoraPortugal
  4. 4.Castilla-La Mancha UniversityToledoSpain

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