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Prediction of Cardiac Arrest in Intensive Care Patients Through Machine Learning

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Precision Medicine Powered by pHealth and Connected Health (ICBHI 2017)

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Cardiac arrest is a critical health condition characterized by absence of traceable heart rate, patient’s loss of consciousness as well as apnea, with inhospital mortality of ~80%. Accurate estimation of patients at high risk is crucial to improve not only the survival rate, but also the quality of life as patients who survived from cardiac arrest have severe neurological effects. Existing research has focused on demonstrating static risk scores without taking account patient’s physiological condition. In this study, we are implementing an integrated model of sequential contrast patterns using Multichannel Hidden Markov Model. These models can capture relations between exposure and control group and offer high specificity results, with an average sensitivity of 78%, and have the ability to identify patients in high risk.

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  1. Sandroni C, Nolan J, Cavallaro F, Antonelli M (2007) In-hospital cardiac arrest: incidence, prognosis and possible measures to improve survival. Intensive Care Med 33(2):237–245

    Article  Google Scholar 

  2. Graham R, McCoy, MA, Schultz AM (2015) Committee on the treatment of cardiac arrest: current status and future directions, Board on Health Sciences Policy, Institute of Medicine Strategies to improve Cardiac Arrest Survival: A Time to Act. Washington (DC). National Academies Press (US), 29 Sept 2015

    Google Scholar 

  3. Bergum D, Haugen BO, Nordseth T, Mjølstad OC, Skogvoll E (2015) Recognizing the causes of in-hospital cardiac arrest-A survival benefit. Resuscitation. 97:91–96

    Article  Google Scholar 

  4. Nolan JP, Soar J, Smith GB, Gwinnutt C, Parrott F, Power S et al (2014) Incidence and outcome of in-hospital cardiac arrest in the United Kingdom National Cardiac Arrest Audit. Resuscitation. 85(8):987–992

    Article  Google Scholar 

  5. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG et al (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):E215–E220

    Article  Google Scholar 

  6. Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman LW, Moody G et al (2011) Multiparameter intelligent monitoring in intensive care ii: a public-access intensive care unit database. Crit Care Med 39(5):952–960

    Article  Google Scholar 

  7. Smith AF, Wood J (1998) Can some in-hospital cardiorespiratory arrests be prevented? A prospective survey. resuscitation. 37(3):133–137

    Google Scholar 

  8. Hodgetts TJ, Kenward G, Vlachonikolis IG, Payne S, Castle N (2002) The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team. Resuscitation. 54(2):125–131

    Article  Google Scholar 

  9. McBride J, Knight D, Piper J, Smith GB (2005) Long-term effect of introducing an early warning score on respiratory rate charting on general wards. Resuscitation. 65(1):41–44

    Article  Google Scholar 

  10. Ho JC, Park Y, Carvalho CM, Ghosh J (2013) DYNACARE: dynamic cardiac arrest risk estimation. J Mach Learn Res 31:333–341

    Google Scholar 

  11. Ghosh S, Li J, Cao L, Ramamohanarao K (2017) Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns. J Biomed Inf 66:19–31

    Article  Google Scholar 

  12. Longo DL et al (2015) Cardiovascular collapse, cardiac arrest, and sudden cardiac death. In: Harrison’s Principles of Internal Medicine, 19th edn, New York

    Google Scholar 

  13. Sudden cardiac arrest.

  14. Podrid PJ. Overview of sudden cardiac arrest and sudden cardiac death.

  15. American Heart Association. Heart attack or sudden cardiac arrest: How are they different?

  16. Neumar RW, Shuster M, Callaway CW, Gent LM, Atkins DL et al (2015) Part 1: executive summary: 2015 American heart association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 132(18 Suppl 2):S315-167

    Article  Google Scholar 

  17. Arrhythmia. National Heart, Lung, and Blood Institute.

  18. Fuster V et al (2011) Sudden cardiac death in hurst’s the heart, 13th edn. The McGraw-Hill Companies, New York

    Google Scholar 

  19. Goldberger AL et al (2013) Sudden cardiac arrest and sudden cardiac death in clinical electrocardiography: a Simplified Approach, 8th edn. Saunders Elsevier, Philadelphia

    Google Scholar 

  20. Association. AH. Ejection fraction heart failure measurement.

  21. Riggin EA. Allscripts EPSi. Mayo Clinic, Rochester, Minn

    Google Scholar 

  22. Rohren CH (expert opinion). Mayo Clinic, Rochester, Minn

    Google Scholar 

  23. Vink G, Frank LE, Pannekoek J, van Buuren S (2014) Predictive mean matching imputation of semicontinuous variables. Stat Neerl 68(1):61–90

    Article  MathSciNet  Google Scholar 

  24. Klema J, Novakova L, Karel F, Stepankova O (2008) Sequential data mining: A comparative case study in development of atherosclerosis risk factors. Syst Man Cybern Part C Appl Rev IEEE Trans 38(1):3–15

    Article  Google Scholar 

  25. Baralis E, Bruno G, Chiusano S, Domenici VC, Mahoto NA, Petrigni C (2010) Analysis of medical pathways by means of frequent closed sequences. In: Knowledge-based and intelligent information and engineering systems, pp. 418–425

    Chapter  Google Scholar 

  26. Berlingerio M, Bonchi F, Giannotti F, Turini F (2007) Time-annotated sequences for medical data mining. In: Seventh IEEE international conference on data mining workshops. IEEE, pp 133–138

    Google Scholar 

  27. Audhkhasi K, Osoba O, Kosko B (2013) Noisy hidden Markov models for speech recognition. In: The 2013 international joint conference on neural networks (IJCNN)

    Google Scholar 

  28. Cao L, Ou Y, Yu PS (2012) Coupled behavior analysis with applications. IEEE Trans Knowl Data Eng 24(8):1378–1392

    Article  Google Scholar 

  29. Masoudi S, Montazeri N, Shamsollahi MB, Ge D, Beuche A, Pladys P, et al (2013) Early detection of apnea-bradycardia episodes in preterm infants based on coupled hidden Markov model. IEEE international symposium on signal processing and information technology

    Google Scholar 

  30. Zhou H, Chen J, Dong G, Wang H, Yuan H (2016) Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model. Mech Syst Signal Process 66–67:568–581

    Article  Google Scholar 

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Correspondence to E. Akrivos .

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Akrivos, E., Papaioannou, V., Maglaveras, N., Chouvarda, I. (2018). Prediction of Cardiac Arrest in Intensive Care Patients Through Machine Learning. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore.

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