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Critical Events in Mechanically Ventilated Patients

  • Filipe PortelaEmail author
  • Manuel F. Santos
  • José Machado
  • António Abelha
  • Álvaro Silva
  • Fernando Rua
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 445)

Abstract

Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a pre-defined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.

Keywords

Critical events Intensive care Intcare Ventilated patients Data acquisition Real-time Streaming data Interoperability 

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References

  1. 1.
    Portela, F., Gago, P., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Implementing a Pervasive Real-time Intelligent System for Tracking Critical Events with Intensive Care Patients. In: IJHISI - International Journal of Healthcare Information Systems and Informatics. Issue 4, pp 1-16. IGI Global (2013)Google Scholar
  2. 2.
    Silva, Á., Cortez, P., Santos, M.F., Gomes, L., Neves, J.: Rating organ failure via adverse events using data mining in the intensive care unit. In: Artificial Intelligence in Medicine 43, 179-193 (2008)Google Scholar
  3. 3.
    Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Pervasive and intelligent decision support in Intensive Medicine–the complete picture. In: Information Technology in Bio-and Medical Informatics, pp. 87-102. Springer (2014)Google Scholar
  4. 4.
    Portela, F., Aguiar, J.,, Santos, M. F., Silva, A. Rua, F.: Pervasive Intelligent Decision Support System - Technology Acceptance in Intensive Care Units. In: Springer (ed.) Advances in Intelligent Systems and Computing. Springer (2013)Google Scholar
  5. 5.
    Curtis, J.R., Engelberg, R.A., Bensink, M.E., Ramsey, S.D.: End-of-life care in the intensive care unit: can we simultaneously increase quality and reduce costs? In: American journal of respiratory and critical care medicine 186, 587-592 (2012)Google Scholar
  6. 6.
    Keegan, M.T., Gajic, O., Afessa, B.: Severity of illness scoring systems in the intensive care unit. In: Critical care medicine 39, 163 (2011)Google Scholar
  7. 7.
    Evans, R.S., Johnson, K.V., Flint, V.B., Kinder, T., Lyon, C.R., Hawley, W.L., Vawdrey, D.K., Thomsen, G.E.: Enhanced notification of critical ventilator events. In: Journal of the American Medical Informatics Association 12, 589-595 (2005)Google Scholar
  8. 8.
    Centers for Disease Control and Prevention, http://www.cdc.gov/
  9. 9.
    Alasad, J.: Managing technology in the intensive care unit: the nurses’ experience. In: International Journal of Nursing Studies 39, 407-413 (2002)Google Scholar
  10. 10.
    Fauci, A.S.: Harrison’s Principles of Internal Medicine, 17e. Silverchair Science: Minion (2008)Google Scholar
  11. 11.
    Tehrani, F.T.: Automatic control of mechanical ventilation. Part 2: the existing techniques and future trends. In: Journal of clinical monitoring and computing 22, 417-424 (2008)Google Scholar
  12. 12.
    Santos, M.F., Portela, F., Vilas-Boas, M., Machado, J., Abelha, A., Neves, J.: INTCARE - Multi-agent approach for real-time Intelligent Decision Support in Intensive Medicine. In: 3rd International Conference on Agents and Artificial Intelligence (ICAART) (2011)Google Scholar
  13. 13.
    Portela, F., Gago, P., Santos, M. F., Silva, A., Rua, F.: Intelligent and Real Time Data Acquisition and Evaluation to Determine Critical Events in Intensive Medicine. In: HCist’2012 - International Conference on Health and Social Care Information Systems and Technologies. Elsevier (2012)Google Scholar
  14. 14.
    Portela, F. Veloso, R., Oliveira, S., Santos, M.F., Abelha, A., Machado, J., Silva, A. Rua, F.: Predict hourly patient discharge probability in Intensive Care Units using Data Mining. In: Indian Journal of Science and Technology. Indian Society for Educat (2016). (accepted for publication)Google Scholar
  15. 15.
    Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á.: Pervasive and Intelligent Decision Support in Critical Health Care Using Ensembles. In: Information Technology in Bio-and Medical Informatics, pp. 1-16. Springer Berlin Heidelberg (2013)Google Scholar
  16. 16.
    Portela, F., Santos, M.F., Machado, J., Silva, Á., Rua, F., Abelha, A.: Intelligent Data Acquisition and Scoring System for Intensive Medicine. In: Springer (ed.) Lecture Notes in Computer Science - Information Technology in Bio- and Medical Informatics, vol. 7451/2012, pp. 1-15, Viena, Austria (2012)Google Scholar
  17. 17.
    Hoo, G.W.S.: Barotrauma and Mechanical Ventilation. pp. 24. Medscape (2009)Google Scholar
  18. 18.
    Oliveira, S., Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Predicting Plateau Pressure in Intensive Medicine for Ventilated Patients. In: New Contributions in Information Systems and Technologies, pp. 179-188. Springer (2015)Google Scholar
  19. 19.
    Oliveira, S. Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Characterizing Barotrauma Patients in ICU - Clustering Data Mining using ventilator variables. In: Springer (ed.) Lecture Notes in Artificial Intelligence (LNAI), Volume 9273, 2015, pp 122-127. Springer (2015)Google Scholar
  20. 20.
    Oliveira, S. Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Intelligent Decision Support to predict patient Barotrauma risk in Intensive Care Units. In: Elsevier (ed.) In: Procedia Technology, Volume 64, 2015, pp 626-634. Elsevier (2015)Google Scholar
  21. 21.
    Cardoso, L., Marins, F., Portela, F., Santos, M., Abelha, A., Machado, J.: The Next Generation of Interoperability Agents in Healthcare. In: International journal of environmental research and public health 11, 5349-5371 (2014)Google Scholar
  22. 22.
    Marins, F., Cardoso, L., Portela, F., Santos, M.F., Abelha, A., Machado, J.: Improving High Availability and Reliability of Health Interoperability Systems. In: New Perspectives in Information Systems and Technologies, Volume 2, pp. 207-216. Springer (2014)Google Scholar
  23. 23.
    Hooda, J.S., Dogdu, E., Sunderraman, R.: Health Level-7 compliant clinical patient records system. pp. 259-263. ACM (2004)Google Scholar
  24. 24.
    Portela, F., Oliveira, S., Santos, M.F., Abelha, A. Machado, J.: A Real-Time Intelligent System for tracking patient condition. In: Springer (ed.) LNCS - Ambient Intelligence for Health, vol. 9456, Springer (2015)Google Scholar
  25. 25.
    Santos, M.F., Portela, F.: Enabling Ubiquitous Data Mining in Intensive Care - Features selection and data pre-processing. In: publication, a.t. (ed.) 13th International Conference on Enterprise Information Systems, pp. 6, Beijing, China (2011)Google Scholar
  26. 26.
    Portela, F., Santos, M. F., Abelha, A., Machado, J., Rua F., Silva, A.: Real-time Decision Support using Data Mining to predict Blood Pressure Critical Events in Intensive Medicine Patients. In: Springer (ed.) Lecture Notes in Computer Science (LNCS) - Ambient Intelligence for Health, vol. 9456, Springer (2015)Google Scholar
  27. 27.
    Portela, F., Santos, M. F., Abelha, A., Machado, J., Rua F., Silva, A.: Preventing Patient Cardiac Arrhythmias by Using Data Mining Techniques. In: 2014 IEEE Conference on Biomedical Engineering and Sciences (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Filipe Portela
    • 1
    • 2
    Email author
  • Manuel F. Santos
    • 1
  • José Machado
    • 1
  • António Abelha
    • 1
  • Álvaro Silva
    • 3
  • Fernando Rua
    • 3
  1. 1.Algoritmi Research CentreUniversity of MinhoBragaPortugal
  2. 2.ESEIGPorto PolytechnicPortoPortugal
  3. 3.Intensive Care UnitCentro Hospitalar do PortoPortoPortugal

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