Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand
Abstract
Background: Cerebrovascular diseases or stroke tend to cause high mortality in Thailand. An essential responsibility of a hospital is the development of medical care to support the safety of patients. For this purpose, a smartphone application was developed for the risk assessment and emergency system for stroke treatment in a hospital in Thailand.
Methods: The proposed application involved the risk assessment related to the occurrence of stroke evaluated by the health status and face image using analytical geometry and face detection technology. The social network Application Programming Interface (API), LINE Notify API, and Global Positioning System (GPS) were used to inform the Stroke team in the Suratthani hospital about emergency cases, followed their requirement in 2020.
Results: From the testing, the facial angulation classification, calculated using a support vector machine (SVM), had 92.38% accuracy. The system also provided an emergency call and text messaging that includes patient’s current location and personal information to the stroke team directly, which gave an opportunity for the patient to receive treatment quickly.
Conclusion: The emergency system can help quickly perform the risk assessment of stroke. Our proposed system provides automated management.
2. Suwanwela N C (2014). Stroke Epidemiology in Thailand. J Stroke, 16(1): 1-7.
3. Sacco RL (1995). Risk factors and outcomes for is-chemic stroke. Neurology,45(2 Suppl 1): S10-4.
4. Raul G, Nogueira, Fabricio O, et al (2017). The FAST-ED App: A Smartphone Platform for the Field Triage of Patients with Stroke. Stroke, 48(5):1278-1284.
5. Zainab Pirani, Niha Tasbi, Rehan Fakir, et al (2017). Assistive application for the people with mini-brain stroke. 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2017: 1-6.
6. Nam HS, Heo J, Kim J, et al (2014). Development of smartphone application that aids stroke screening and identifying nearby acute stroke care hospitals. Yonsei Med J, 55(1): 25-9.
7. Laura García, Jesús Tomás, Lorena Parra, et al (2019). An m-health application for cerebral stroke detec-tion and monitoring using cloud services. Interna-tional Journal of Information Management, 45: 319-327.
8. Chulalongkorn Stroke Center (2015). CU Stroke Application. PUN CORPORATION CO., LTD. Available from: https://www.androidblip.com/dev/pun-corporation-co-ltd___80a3f4739161185cf73bb39db7e7a01d9e8c976fe9c6271a5c099d718f23a300.html
9. Khon Kaen University (2017). Fast Track (Stroke KKU) Application. Available from: https://play.google.com/store/apps/details?id=co.th.digix.stroke&hl=en&gl=US
10. Robert L Dickson, Dineth Sumathipala, Jennifer Reeves (2016). Stop Stroke© Acute Care Coordi-nation Medical Application: A Brief Report on Postimplementation Performance at a Primary Stroke Center. J Stroke Cerebrovasc Dis, 25(5):1275-1279.
11. Juan M Calleja-Castillo, Gina Gonzalez-Calderon (2018). WhatsApp in Stroke Systems: Current Use and Regulatory Concerns. Front Neurol, 9: 388.
12. Oi-Mean Foong, Kah-Wing Hong, Suet-Peng Yong (2016). Droopy Mouth Detection Model in stroke warning. 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), 2016: 616-621.
13. Chuan-Yu Chang, Man-Ju Cheng, Matthew Huei-Ming Ma (2018). Application of Machine Learn-ing for Facial Stroke Detection. 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 2018: 1-5.
14. Vathesatogkit P, Woodward M, Tanomsup S, et al (2012). Cohort profile: the electricity generating au-thority of Thailand study. Int J Epidemiol, 41: 359–65.
15. Kingkaew N and Antadech T (2019). Cardiovascular risk factors and 10-year CV risk scores in adults aged 30-70 years old in AmnatCharoen Province, Thailand. Asia-Pacific J Sci Technol, 24(4).
16. Vilaiwatanakorn K, Vathesatogkit P, Yingchon-charoen T, et al (2015). Accuracy of 10 year-risk calculation for first atherosclerotic cardiovascular event from new pooled cohort equations and WHO risk calculation in EGAT population. Eu-rop Heat J, 36: 805-805.
17. Quinlan JR (1993). C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., California, USA, pp. 17-26.
18. Quinlan JR (1996). Improved Use of Continuous Attributes in C4.5. Journal of Artificial Intelligence Re-search, 4: 77-90.
19. John Platt (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Ma-chines. Microsoft Research. Available from: https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/
20. Montaño Moreno JJ, Palmer Pol A, et al (2013). Us-ing the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4): 500–6.
Files | ||
Issue | Vol 51 No 4 (2022) | |
Section | Original Article(s) | |
DOI | https://doi.org/10.18502/ijph.v51i4.9240 | |
Keywords | ||
Stroke Support vector machine Face detection Application Screening |
Rights and permissions | |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |