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系統識別號 U0002-0109201616584500
中文論文名稱 智慧臨床醫療輔助系統在神經內科門診頭痛初診應用
英文論文名稱 Wisdom Clinical Assistance System to Neurology Clinic Diagnosis Applications
校院名稱 淡江大學
系所名稱(中) 資訊工程學系全英語碩士班
系所名稱(英) Master’s Program, Department of Computer Science and Information Engineering (English-taught program
學年度 104
學期 2
出版年 105
研究生中文姓名 楊哲維
研究生英文姓名 Che-Wei Yang
電子信箱 ddd80101@gmail.com
學號 603780049
學位類別 碩士
語文別 英文
口試日期 2016-07-28
論文頁數 79頁
口試委員 指導教授-葛煥昭
委員-王署君
委員-施俊哲
中文關鍵字 臨床醫療系統  敏捷式開發 
英文關鍵字 Clinical support system  Agile Software Development 
學科別分類 學科別應用科學資訊工程
中文摘要 偏頭痛一直以來都是國內不可忽視病痛之一,根據研究顯示,國內患有頭痛疾病的人也佔有一定比例,雖然偏頭痛經過一段時間可以被緩解,但是由於偏頭痛的病患大多正值青壯年,而且頭痛病患沒辦法預知何時會再次復發,發作時可能會造成病患嚴重失能的狀態,因此偏頭痛對日常生活、工作及社交影響很大,因此患者求醫的比例越來越高。
在本次研究中,將病人本身填寫的紙本初診問卷,轉換為智慧型的初診問卷,藉此來幫助臨床醫師在初診判斷時有更準確的數據參考以及自動計算各項測驗分數來協助做診斷,並且大幅改善臨床醫師在看初診病人的時間,解決護理人員需要檢查初診問卷以及計算分數的問題,降低病人填寫時可能會出現的錯誤,另外並提供完善的資料保存,確保資料不會因人為因素導致流失,以供後續醫院可以藉此大量數據來深入研究,找出偏頭痛可能會有的隱藏因子來提供給臨床醫師協助診斷用,讓偏頭痛可以被提早診斷出,並且能夠在早期治療及提升治療成效。
最後,希望能利用本研究之研究成果為案例,提供各醫院對於神經內科醫師看診時有所幫助,有效的提升臨床醫師看診的效率,改善整體就診環境,大幅降低護理人員耗費在初診病患身上的時間,而收集的所有初診問卷在系統化之後所產生的相關資料,更可以作為臨床醫學上巨量數據(Big Data)研究的基礎資料。
英文摘要 Migraine has always been the one of the serious illness in Taiwan. According to the studies, a large amount of people suffer from headache. In this study, most migraine patients are young adults, although migraine can get remission after a period of times, patient can’t predict the frequency of migraine attacks. People with migraine may be in serious disability status. Therefore, migraine impacts our daily life, work and social. The seeking treatment patient's proportion are getting increase.
In this study, we use a newly diagnosed patient questionnaire and patient's electronic records to analysis and help doctor make the diagnosis. We improve the diagnosed efficiency and reduce the time when the doctor make a diagnosis. In addition, we also expect to identify migraine which may have a hidden factor available to assist clinicians with diagnosis. If migraine can be diagnosed early, the way of treatment can be improved much efficiently.
論文目次 Table of Contents
Chapter 1 Introduction 1
1.1. Background 1
1.2. Motivation 2
1.3. Purpose 3
Chapter 2 Literature Review 5
2.1. Big Data 5
2.2. Database 7
2.3. Agile Software Development 8
2.4. Taiwan Food and Drug Administration 10
2.5. Migraine and Measuring Tools 12
2.5.1. Headache Questionnaire 13
2.5.2. Beck Depression Inventory 14
2.5.3. Pittsburgh Sleep Quality Index 14
2.5.4. Restless Leg Syndrome 15
2.5.5. Migraine Disability Assessment 16
2.5.6. Anxiety and Depression Scale 16
Chapter 3 Method 18
3.1. Interview and Clinical Observation 20
3.2. Questionnaire Requirements Analysis 21
3.3. Preliminary System Design 22
3.4. System Implementation 24
3.5. System Application 25
3.6. Advanced System Design 25
3.7. System Maintenance 29
3.8. Discussion 29
Chapter 4 Results 30
4.1. User Application Management 30
4.2. Questionnaire Application Management 32
4.3. Clinical Effectiveness Evaluation 48
Chapter 5 Conclusion and Future Prospects 51
5.1. Conclusion 51
5.2. Prospects 53
Reference 55
Appendix 57

List of Figures
Figure 1. Study Flowchart 20
Figure 2. System Analysis Flowchart 23
Figure 3. System requirement list 26
Figure 4. System modifications 27
Figure 5. System modifications 27
Figure 6. The number of users 28
Figure 7. The number of migraine users 28
Figure 8. Clinicians sign-in page 31
Figure 9. Patients sign-in page 31
Figure 10. User sign out 31
Figure 11. Main function page 32
Figure 12. Patients list page 32
Figure 13. Score statistics page 33
Figure 14. Score statistics page 33
Figure 15. RLS scale 34
Figure 16. MIDAS scale 35
Figure 17. HIT-6 Score 36
Figure 18. The Hospital Anxiety and Depression Scale 37
Figure 19. BDI scale 38
Figure 20. WPI scale 38
Figure 21. SympS score 39
Figure 22. FIQR scale 40
Figure 23. Sleep scale 41
Figure 24. First-visit patient basic information 42
Figure 25. First-visit patient headache status 42
Figure 26. Headache omen 42
Figure 27. Visual status 43
Figure 28. NRS 43
Figure 29. Pulse 43
Figure 30. N.V.S.L.O 44
Figure 31. Family History 45
Figure 32. PA 45
Figure 33. Yawning 45
Figure 34. Headache position 46
Figure 35. Headache position 46
Figure 36. Women issue 47
Figure 37. Medicine 47
Figure 38. Clinical Impression 48
Figure 39. Clinical Impression 48
Figure 40. Patient’s Time Comparison 49
Figure 41. Patient’s Time Comparison 50


List of Table
Table 1. Different data analysis between traditional business and big data 6
Table 2. Medical Device Document 11

參考文獻 Reference
[1] Wei-Ta, Chen, Shuu-Jiun, Wang. Alteration of visual cortical excitabilities in migraine 2009, pp. 44-48.
[2] Juang KD, Wang SJ, Fuh JL, Lu SR, Su TP. Comorbidity of depressive and anxiety disorders in chronic daily headache and its subtypes. 2000, pp. 818-823.
[3] Hung-Jung Lin, The Study on Risk Factors of Patient Safety -- An Empirical Study of the Emergency Departments of Large-Scaled Hospitals in Taiwan. 2003.
[4] Hsien-Ming Lin, The Implications and Reflections of Applying Big Data to Social Science Research, 2016.
[5] Formosan J Med, Big Data Analysis in Medical Care. pp. 652-661.
[6] D.A. Adjeroh and K.C. Nwosu, Multimedia Database Management Requirements and Issues, IEEE Multimedia, July-September.
[7] Martin, Robert C., Agile Software Development, Principles, Patterns, and Practices, 1st edition, Prentice Hall, 2002.
[8] Hui-Lan Tsai, The Study of Agile Methodology for Information Service Industry in Taiwan, 2014.
[9] Medical Devices; Medical Device Data System, Federal Register, Vol.76, No.31, 2011.
[10] What is Migraine, http://www.tph.mohw.gov.tw/?aid=509&pid=75&page name=detail&iid=514.
[11] Migraine, http://www.taiwanheadache.com.tw.
[12] Migraine and Sleep, http://www.taiwanheadache.com.tw.
[13] Aigmond AS, Snaith RP: The Hospital Anxiety and Depression Scale. 1983, pp.67-70.
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