Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand
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 sys...
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2022-04-01
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| Series: | Iranian Journal of Public Health |
| Subjects: | |
| Online Access: | https://ijph.tums.ac.ir/index.php/ijph/article/view/22879 |
| _version_ | 1811291512614420480 |
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| author | Siwipa Pruitikanee Jinda Kongcharoen Supattra Puttinaovarat Thotsaphon Yaifai Sasikorn Chaitada |
| author_facet | Siwipa Pruitikanee Jinda Kongcharoen Supattra Puttinaovarat Thotsaphon Yaifai Sasikorn Chaitada |
| author_sort | Siwipa Pruitikanee |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-04-13T04:30:34Z |
| format | Article |
| id | doaj.art-8dae87c7f894479581c6c9b69f0b5486 |
| institution | Directory Open Access Journal |
| issn | 2251-6085 2251-6093 |
| language | English |
| last_indexed | 2024-04-13T04:30:34Z |
| publishDate | 2022-04-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Iranian Journal of Public Health |
| spelling | doaj.art-8dae87c7f894479581c6c9b69f0b54862022-12-22T03:02:20ZengTehran University of Medical SciencesIranian Journal of Public Health2251-60852251-60932022-04-0151410.18502/ijph.v51i4.9240Stroke Risk Assessment and Emergency Mobile Application in a Hospital in ThailandSiwipa Pruitikanee0Jinda Kongcharoen1Supattra Puttinaovarat2Thotsaphon Yaifai3Sasikorn Chaitada4Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, ThailandFaculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, ThailandFaculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, ThailandFaculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, ThailandFaculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Thailand 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. https://ijph.tums.ac.ir/index.php/ijph/article/view/22879StrokeSupport vector machineFace detectionApplicationScreening |
| spellingShingle | Siwipa Pruitikanee Jinda Kongcharoen Supattra Puttinaovarat Thotsaphon Yaifai Sasikorn Chaitada Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand Iranian Journal of Public Health Stroke Support vector machine Face detection Application Screening |
| title | Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand |
| title_full | Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand |
| title_fullStr | Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand |
| title_full_unstemmed | Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand |
| title_short | Stroke Risk Assessment and Emergency Mobile Application in a Hospital in Thailand |
| title_sort | stroke risk assessment and emergency mobile application in a hospital in thailand |
| topic | Stroke Support vector machine Face detection Application Screening |
| url | https://ijph.tums.ac.ir/index.php/ijph/article/view/22879 |
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