The Journal of Korean Physical Therapy

The Korean Society of Physical Therapy (대한물리치료학회)
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Association Analysis of Comorbidity of Cerebral Infarction Using Data Mining

데이터 마이닝을 활용한 뇌경색증과 동반되는 질환의 연관성 분석

  • Lee, In-Hee (Department of Medical Informatics, School of Medicine, Keimyeung University) ;
  • Shin, A-Mi (Department of Medical Informatics, School of Medicine, Keimyeung University) ;
  • Son, Chang-Sik (Department of Medical Informatics, School of Medicine, Keimyeung University) ;
  • Park, Hee-Joon (Department of Medical Informatics, School of Medicine, Keimyeung University) ;
  • Kim, Joong-Hwi (Department of Physical Therapy, Kang Hospital) ;
  • Park, Sang-Young (Department of Physical Therapy, NamSan Hospital) ;
  • Choi, Jin-Ho (Department of Physical Therapy, College of Health and Therapy, Daegu Haany University) ;
  • Kim, Yoon-Nyun (Department of Internal Medicine, School of Medicine, Keimyeung University)
  • 이인희 (계명대학교 의과대학 의료정보학교실(계명대학교 동산병원 물리치료실, 계명대학교 보건의료정보기술연구소)) ;
  • 신아미 (계명대학교 의과대학 의료정보학교실(계명대학교 동산병원 물리치료실, 계명대학교 보건의료정보기술연구소)) ;
  • 손창식 (계명대학교 의과대학 의료정보학교실(계명대학교 동산병원 물리치료실, 계명대학교 보건의료정보기술연구소)) ;
  • 박희준 (계명대학교 의과대학 의료정보학교실(계명대학교 동산병원 물리치료실, 계명대학교 보건의료정보기술연구소)) ;
  • 김중휘 (강병원 물리치료실) ;
  • 박상영 (남산병원 물리치료실) ;
  • 최진호 (대구한의대학교 보건치료대학 물리치료학과) ;
  • 김윤년 (계명대학교 의과대학 내과학교실(계명대학교 동산병원 심장내과))
  • Received : 2009.12.05
  • Accepted : 2010.02.08
  • Published : 2010.02.25
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Abstract

Purpose: The purpose of this study was to apply association rule mining to explore the labyrinthine network of cerebral infarction comorbidity and basic data supply to develop cutting-edge physical therapy protocols for cerebral infarction with comorbidity Methods: From clinic records of enrollees of A Hospital in D city, patients over 18 years of age with cerebral infarction and cerebral infarction comorbidity were recruited as a case group. All diagnoses of that hospital were categorized according to the "International Classification of Disease (ICD)" diagnosis system. We extracted code I63 from the "Korea Classification of Disease (KCD)-4". Associated rule mining was done with a priori modeling and Web nodes to examine the strengths of associations among those diagnoses. The support and confidence values of associated rule mining results were examined. Results: The subjects of this study were 2,267 cerebral infarction patients. E11 (Non-insulin-dependent diabetes mellitus), E78 (Disorders of lipoprotein metabolism and other lipidaemias), G81 (Hemiplegia), I10 (Essential hypertension), and K29 (Gastritis and duodenitis) were high frequency diagnoses, being found in 10% or more of total diagnoses of cerebral infarction from frequency analysis results. The highest frequency diagnosis was 1,042 (46.0%) for I10. The second most frequent diagnosis was for E11(21.5%) while the third most frequent diagnosis was E78 (20.2%). Results from a priori modeling and Web nodes indicated that cerebral infarction has a strong association withessential hypertension, non-insulin-dependent diabetes mellitus, disorders of lipoprotein metabolism and other lipidaemias. Conclusion: Cerebral infarction is associated with hypertension, diabetes mellitus, and disorders of lipoprotein metabolism and other lipidaemias. The result of this study will be helpful to clinicians treating patients with cerebral infarction.

Keywords

References

  1. Korea national statistical office. Annual report on the cause of death statistics. 2008, Retrieved August 2009.
  2. Lee IS. A case study on the group counseling for the stroke elderly. Korean Society of Welfare for the Aged. 2001;12:209-27.
  3. Mower DM. Brain attack. Treating acute ischemic CVA. Nursing. 1997;27(3):34-9.
  4. Yu KH, Bae HJ, Kwon SU et al. Analysis of 10,811 cases with acute ischemic stroke from Korean stroke registry: Hospital-based multicenter prospective registration study. J Korean Neurol Assoc. 2006;24(6):535-43.
  5. Lee MK, Kim JM, Kim WH, The effect of PNF leg patterns on activation of biceps and triceps in stroke patients. J Kor Soc Phys Ther. 2009;21(1):1-7. https://doi.org/10.1589/jpts.21.1
  6. Siqueira Nete JI, Santos AC, Cabete Fabio SR et al. Cerebral infarction in patients aged 15 to 40 years. Stroke. 1996;27(11):2016-9. https://doi.org/10.1161/01.STR.27.11.2016
  7. Kristensen B, Malm J, Carlberg B et al. Epidemiology and etiology of ischemic stroke in young adults aged 18 to 44 years in northern Sweden. Stroke. 1997;28(9):1702-9. https://doi.org/10.1161/01.STR.28.9.1702
  8. Hardie K, Hankey GJ, Jamrozik K et al. Ten-year risk of first recurrent stroke and disability after first-ever stroke in the Perth Community Stroke Study. Stroke. 2004:35(3);731-35. https://doi.org/10.1161/01.STR.0000116183.50167.D9
  9. Lee IH, Kim SH. Stroke Rehabilitation. Seoul, Epublic, 2004:3-27.
  10. Song JM, Kim SM. The effect of aquatic exercise on the improvement of physical and pulmonary function after stroke. J Kor Soc Phys Ther. 2009;21(2):15-22.
  11. Lee JH, Kwon YJ, Kim K. The effect of chest expansion and pulmonary function of stroke patients after breathing exercise. J Kor Soc Phys Ther. 2009;21(3):25-32.
  12. Chung SJ, Kim JS, Lee CH. Roles of risk factor changes,preceding infection, exposure to coldness, and psychologic stress. J Kor Neurol Ass. 1998;16(5):609-15.
  13. Yang JI, Rah UW, Moon HW et al. Studies of risk factors in stroke patients. J Korean Acad Rehab Med. 1997;21(4):643-51.
  14. Bae HS, Cho DH, Suk KH et al. Data mining using SAS Enterprise Miner. 2nd ed. Seoul, Kyowoosa, 2008:77-86.
  15. Tai YM, Chiu HW. Comorbidity study of ADHD: Applying association rule mining (ARM) to National Health Insurance Database of Taiwan. Int J M Inform. 2009;78(12):e75-83. https://doi.org/10.1016/j.ijmedinf.2009.09.005
  16. Greenlund KJ, Giles WH, Keenan NL et al. Physician advice, patient actions, and health-related quality of life in secondary prevention of stroke through diet and exercise. Stroke. 2002;33(2):565-70. https://doi.org/10.1161/hs0202.102882
  17. Kang HC, Han ST, Choi JH et al. Datamining with SAS Enterprise Miner 4.0; methodology and application. 3rd ed. Seoul, Jayuacademi, 2001:155-74.
  18. Huh J, Jeong KS, Huh SH et al. Clementine 7 manual. Seoul, Datasolution, 2003:188-91.
  19. Huh MH, Lee YG. Data mining modeling and case. 2nd ed. Seoul, Hannarae, 2008:171-250.
  20. Lee MS. Relating factors and health promoting behaviors of stroke patients. Pusan Catholic University. Dissertation of master's Degree. 2004.
  21. Nam SW, Chung CS, Choi YC et al. Vascular risk factors related to stoke types in hypertensive subjects. J Korean Neurol Assoc. 1995;13(2):171-6.
  22. Bogousslavsky J, Castillo V, Kumral E et al. Stroke subtypes and hypertension. Primary hemorrhage vs infarction, large-vs small-artery disease. Arch Neurol. 1996;53(3):265-9. https://doi.org/10.1001/archneur.1996.00550030077025
  23. Lestro Henriques I, Bogousslavsky J, van Melle G. Predictors of stroke pattern in hypertensive patients. J Neurol Sci. 1996;144(1-2):142-6. https://doi.org/10.1016/S0022-510X(96)00218-3
  24. Barrett-Connor E, Khaw KT. Diabetes mellitus: An independent risk factor for stroke? Am J Epidemiol. 1988;128(1):116-23.
  25. Lee YH. Differences of risk factors between hemorrhagic and ischemic strokes in hypertensive patients. Gyungsang National University. Dissertation of Master's Degree. 2001.
  26. Kang MS. Associated factors with degree of disability of stroke patients in a rural community. Dongguk University. Dissertation of Master's Degree. 2009.
  27. Kim KT, Kang MJ, Lee HJ et al. Clinical factors associated with functional status at discharge in stroke patients. J Korean Acad Rehab Med. 2003;27(3):300-8.
  28. American Diabetes Association. Standards of medical care in diabetes-2008. Diabetes Care. 2008;31(suppl 1):S12-54.
  29. Pescatello LS, Franklin R, Farard WB et al. American college of sports medicine position stand. Exercise and hypertension. Medicine and Science in Sports and Exercise. 2004;36(3):535-53.