Integrated System for Clinical Decision Support in Emergency Stroke Care

  • Artur PrzelaskowskiEmail author
  • Ewa Sobieszczuk
  • Rafal Jóźwiak
  • Dominika Życka-Malesa
  • Ihor Mykhalevych
  • Katarzyna Sklinda
  • Antoni Sobkowicz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 471)


In this work we present a system for decision support in emergency stroke care developed as a part of integrated health-care environment. Presented system consists of four interconnected modules, each responsible for supporting medical staff in making correct decision at each step of emergency stroke care: Extra Rapid Transport module, Neurological Diagnosis module, Ischemic Stroke Confirmation module and Thrombolytic Therapy module. Initial experiments were reported with concluded clinical remarks.


Clinical decision support Computerized emergency systems Stroke diagnosis 



This publication was funded by the National Science Centre (Poland) based on the decision DEC-2011/03/B/ST7/03649.


  1. 1.
    El-Koussy, M., Schroth, G., Brekenfeld, C., Arnold, M.: Imaging of acute ischemic stroke. Eur. Neurol. 72(5–6), 309–316 (2014)CrossRefGoogle Scholar
  2. 2.
    Flynn, D., Nesbitt, D.J., Ford, G.A., McMeekin, P., Rodgers, H., Price, C., Kray, C., Thomson, R.G.: Development of a computerised decision aid for thrombolysis in acute stroke care. BMC Med. Inf. Decis. Mak. 7, 15:6 (2015)Google Scholar
  3. 3.
  4. 4.
    Jauch, E.C., Saver, J.L., Adams Jr., H.P., Bruno, A., Connors, J.J., Demaerschalk, B.M., Khatri, P., McMullan Jr., P.W., Qureshi, A.I., Rosenfield, K., Scott, P.A., Summers, D.R., Wang, D.Z., Wintermark, M., Yonas, H.: Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 44(3), 870–947 (2013)CrossRefGoogle Scholar
  5. 5.
    Jones, S.S., Rudin, R.S., Perry, T., Shekelle, P.G.: Health information technology: an updated systematic review with a focus on meaningful use. Ann. Intern. Med. 7;160(1):48–54 (2014)Google Scholar
  6. 6.
    Kendall, J., Dutta, D., Brown, E.: Reducing delay to stroke thrombolysis—lessons learnt from the Stroke 90 Project. Emerg. Med. J. 32(2), 100–104 (2015)Google Scholar
  7. 7.
    Kurz, K.D., Ringstad, G., Odland, A., Advani, R., Farbu, E., Kurz, M.W.: Radiological imaging in acute ischaemic stroke. Eur. J. Neurol. 23(Suppl 1), 8–17 (2016)CrossRefGoogle Scholar
  8. 8.
    Lou, M., Safdar, A., Selim, M., et. al.: The HAT Score: a simple grading scale for predicting hemorrhage after thrombolysis. Neurology 71(18), 1417–1423 (2008)Google Scholar
  9. 9.
    McMeekin, P., Flynn, D., Ford, G.A., Rodgers, H., Gray, J., Thompson, R.G.: Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment. BMC Med. Inf. Decis. Mak. 11;15, 90 (2015)Google Scholar
  10. 10.
    Menon, B.K., Campbell, B.C., Levi, C., Goyal, M.: Role of imaging in current acute ischemic stroke workflow for endovascular therapy. Stroke 46(6), 1453–1461 (2015)Google Scholar
  11. 11.
    Miller, D.J., Simpson, J.R., Silver, B.: Safety of thrombolysis in acute ischemic stroke: a review of complications, risk factors, and newer technologies. The Neurohospitalist 1(3), 138–147 (2011)CrossRefGoogle Scholar
  12. 12.
    Pexman, J.H., Barber, P.A., Hill, M.D., Sevick, R.J., Demchuk, A.M., Hudon, M.E., Hu, W.Y., Buchan, A.M.: Use of the Alberta stroke program early CT score (ASPECTS) for assessing CT scans in patients with acute stroke. AJNR Am. J. Neuroradiol. 22(8), 1534–1542 (2001)Google Scholar
  13. 13.
    Przelaskowski, A., Ciszek, B., Jozwiak, R., Domitrz, I., Sobieszczuk, E.: Stroke Bricks—the segments of interests to analyze early stages of the disease evolution. Research report. Warsaw University of Technology (2015)Google Scholar
  14. 14.
    Przelaskowski, A., Ostrek, G., Sklinda, K.: Multiscale extraction of diagnostic content applied for CT brain examinations. Biochem. Biomed. Eng. 29(4), 25–40 (2009)Google Scholar
  15. 15.
    Przelaskowski, A., Sobieszczuk, E., Spalik, P., Domitrz, I., Ostrek, G., Podsiadly-Marczykowska, T., Jóźwiak, R., Jasionowska, M.: Stroke emergency care: a system of computerized decision support for prehospital and in-hospital phases. Research report. Warsaw University of Technology (2015)Google Scholar
  16. 16.
    Rotter, T., Kinsman, L., James, E., Machotta, A., Gothe, H., Willis, J., Snow, P., Kugler, J.: Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database Syst. Rev. 17(3), CD006632 (2010)Google Scholar
  17. 17.
    Saposnik, G., Fang, J., Kapral, M., Tu, J., Mamdani, M., Austin, P., Johnston, S.: On behalf of the investigators of the registry of the Canadian Stroke Network (RCSN) and the Stroke Outcomes Research Canada (SORCan) working group, The iScore predicts effectiveness of thrombolytic therapy for acute ischemic stroke. Stroke 3(5), 1315–1322 (2012)Google Scholar
  18. 18.
    Saver, J.L.: Time is brain-quantified. Stroke 37(1), 263–266 (2006)CrossRefGoogle Scholar
  19. 19.
    Shieh, Y., Chang, C.H., Shieh, M., Lee, T.H., Chang, Y.J., Wong, H.F., Chin, S.C., Goodwin, S.: Computer-aided diagnosis of hyperacute stroke with thrombolysis decision support using a contralateral comparative method of CT image analysis. J. Digit. Imaging 27(3), 392–406 (2014)CrossRefGoogle Scholar
  20. 20.
    Strbian, D., Meretoja, A., Ahlhelm, F.J., Pitkaniemi, J., Lyrer, P., Kaste, M., Engelter, S., Tatlisumak, T.: Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the DRAGON score. Neurology 78(6), 427–432 (2012)Google Scholar
  21. 21.
    Sun, M.C., Chan, J.A.: A clinical decision support tool to screen health records for contraindications to stroke thrombolysis-a pilot study. BMC Med. Inf. Decis. Mak. 14;15(1), 105 (2015)Google Scholar
  22. 22.
    Wardlaw, J.M., Murray, V., Berge, E., del Zoppo, G.J.: Thrombolysis for acute ischaemic stroke. Cochrane Database Syst. Rev. 7, CD000213 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Artur Przelaskowski
    • 1
    Email author
  • Ewa Sobieszczuk
    • 2
  • Rafal Jóźwiak
    • 1
  • Dominika Życka-Malesa
    • 1
  • Ihor Mykhalevych
    • 1
  • Katarzyna Sklinda
    • 3
  • Antoni Sobkowicz
    • 1
  1. 1.Faculty of Mathematics and Information SciencePolitechnika WarszawskaWarszawaPoland
  2. 2.Department of NeurologyMedical University of WarsawWarszawaPoland
  3. 3.Department of Radiology and Diagnostic ImagingMedical Center of Postgraduate EducationWarsawPoland

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