Title

應用案例式推理於血液透析品質評估之輔助系統研究

Translated Titles

Using Case Based Reasoning for Assistance in Hemodialysis Quality Support System

Authors

陳炤文

Key Words
PublicationName

虎尾科技大學工業工程與管理研究所學位論文

Volume or Term/Year and Month of Publication

2007年

Academic Degree Category

碩士

Advisor

Content Language

繁體中文

Chinese Abstract

根據民國94年中華民國腎臟基金會統計全台資料顯示得知,目前需藉由長期透析方式維持健康之末期腎臟病患(End Stage Renal Disease,ESRD)已高達45,718人,其中接受血液透析病患有41,905人,可見血液透析仍是目前末期腎臟病患主要之選擇。台灣透析病患有95.8%需要每週進行三次血液透析,每次透析約需四至五小時,故大部分透析病患之生活品質會受到很大的影響。 尿毒症是腎臟功能極度衰竭引起的症候群,病患必須依靠長期透析治療維持生命。然而,透析治療期間時常發生電解質不平衡及體液的累積,導致病患不適而產生長期透析症候群,甚至引發急性併發症導致死亡。如何讓透析治療除了延續生命以外,提昇到生活品質的訴求,血液透析品質對病患而言不容忽視。 本研究開發「血液透析品質評估輔助系統」,對於血液透析品質有分辨(差、普通、好、很好)的能力。利用長期血液透析累積大量檢驗數據,及相關醫學文獻找出血液透析品質指標:Kt/V、URR、Albumin、Hct,建立血液透析品質因子。藉由案例式推理之運用,建立系統,以及運用粒子搜尋法(Particle Swarm Optimization,PSO)決定案例式推理裡各因子最佳的權重,結果顯示準確度為81.1321%,此系統可提供醫護人員臨床照護參考,對病患狀況作預先處置,使達到適化透析的境界,對透析病患將是一大助益。

English Abstract

Uremia is a symptom occurred by kidneys’ exhaustion. The patient needs long-term dialysis to prolong his life. However, the electrolyte’s disequilibria and the body fluid’s accumulation arisen in the long-term dialysis lead the patient to discomfort, and even to dead. It’s not disregarding for the patient that take which way to prolong his life and improve his life quality further. In this paper, we develop fuzzy Dialysis Quality Evaluation Support System, which has the capability to distinct the dialysis quality(bad, normal, good, excellent). Use the large amount of experiment values, which come from the long-term accumulated dialysis, we studying the medical research and interviewing to the related experts, and find out the dialysis-quality indexes: Kt/V, URR, Albumin, Hct. Further, we use Case Based Reasoning to set up the system and use Particle Swarm Optimization to search the optimal weighting of index to provide the useful information for the medical workers serving in clinical care. The result shows that the accuracy of Case Based Reasoning system reached 81.1321%. It will be helpful for the patient if we can predict the patient’s condition and take the proper treatment, we may prevent his illness getting worse and reach the state of the appropriate dialysis treatment.

Topic Category 管理學院 > 工業工程與管理研究所
工程學 > 工程學總論
社會科學 > 管理學
Reference
  1. 5. 徐慧君 (2002) ,應用案例式推理於顧客關係管理之行銷研究-以化妝品業為例,私立元智大學工業工程與管理研究所,碩士論文。
    連結:
  2. 6. 高芷華、洪冠予(1999),末期腎病變與血液透析,當代醫學,26(5),頁400-401。
    連結:
  3. 10. 陳一傑 (1999) ,應用模糊理論於多專家案例式推理之研究,私立元智大學工業工程研究所,碩士論文。
    連結:
  4. 15. 葉麗雯 (2002) ,供應商產能有限及價格折扣上多產品多供應商最佳化採購決策,私立元智大學工業工程與管理研究所,碩士論文。
    連結:
  5. 19. 謝日章 (2002),柔性計算於生產管理之應用,私立元智大學工業工程與管理研究所,博士論文。
    連結:
  6. 22. Abbas, H. (2002),Comparison of artificial neural network and regression models for estimating software development effort. Information and Software Technology,(44),pp. 911-922.
    連結:
  7. 23. Boyd, R. and Richerson, P. J. (1985). Culture and the Evolutionary Process, University of Chicago Press, Chicago.
    連結:
  8. 24. Dorigo, M. and G. D. Caro. (1999), “Ant Colony Optimization: A New Meta-Heuristic”, The Proceedings of the 1999 Congress, Vol. 2, pp. 1470-1477.
    連結:
  9. 25. Eberhart, R.C. and Kennedy, J. (1995), “A new optimizer using particle swarm theory,” Proc. Sixth International Symposium on Micro Machine and Human Science, pp. 39-43.
    連結:
  10. 27. Eberhart, R.C. and Shi, Y. (2001), “Particle Swarm Optimization: Developments, Applications and Resources,” Proc. IEEE Int. Conf. On Evolutionary Computation, Vol. 1, pp. 81-86.
    連結:
  11. 29. Gotch, F. and Sargent, J. (1985), “A mechanistic analysis of the national cooperative dialysis study”. (NCDS)Kidney Int,(28), pp. 526-534.
    連結:
  12. 30. Hunt, J. (1997), “Case Based Diagnosis and Repair of Software Faults”, Expert Systems, 14(1),Feb. pp.15-23.
    連結:
  13. 33. James, T., Farrington, K. and Roger, G. (1998), ”Oxford Textbook of Clinical Nephrology,” Oxford Medical Publications, (2th ed.), Vol. 3, pp. 2075
    連結:
  14. 36. Kennedy, J. and Spears, W. (1998), “Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator”, In IEEE World Congress on Computational Intelligence, pp. 74–77.
    連結:
  15. 37. Kennedy, J. and Eberhart. R.C. (2001), Swarm Intelligence, Morgan Kaufmann Press.
    連結:
  16. 40. Kwong, C. K. ,G.F. Smith and W.S. Lau (1997), “Application of case-based Reasoning in Injection Moulding”, Journal of Materials Processing Technology, Vol. 63, pp. 463-467
    連結:
  17. 42. Min, L. and Cheng, W. (1999), “A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines”, Artificial Intelligence in Engineering, Volume 13, Number 4, October 1999, pp. 399-403(5)
    連結:
  18. 43. M. Mehdi Owrang O. (1998), “Case Discovery in Case-based Reasoning Systems”, Information Systems Management, pp. 74-78.
    連結:
  19. 44. Miyashita, K. and Sycara, K. (1995), “Cabins: A framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair”, Artificial Intelligence, vol. 76, no. 1-2
    連結:
  20. 47. Reynolds, C. W. (1987), “Flocks, Herds, and Schools: A Distributed Behavioral Model.” ACM SIGGRAPH Computer Graphics. Vol. 21 (4), pp.25-34.
    連結:
  21. 48. Salerno, J. (1997), “Using the particle swarm optimization technique to train a recurrent neural model”, In Proceedings of the Ninth IEEE International Conference on Tools with Artificial Intelligence, pp. 45-49.
    連結:
  22. 53. Suh, M. S., W. C. Jhee, Y. K. Ko, and A. Lee (1998), “A case-based expert system approach for quality design,” Expert Systems With Applications 15, pp.181-190.
    連結:
  23. 55. Watson, I. , and F. Marir (1994), “Case-Based Reasoning: A Review”, Knowledge Engineering Review, Vol.9, No.4.
    連結:
  24. 56. Xiaohui, Hu., Eberhart, R.C. and Shi, Y. (2003), “Engineering Optimization with Particle Swarm,” Swarm Intelligence Symposium, pp.53-57.
    連結:
  25. 57. Xu, L. D. (1996), “An integrated rule- and case-based approach to AIDS initial assessment”, International Journal of Bio-Medical Computing.
    連結:
  26. 一、中文部份
  27. 1. 王世興 (2005) ,建構影響血液透析膜血液透析凝固之預測模式,國立雲林科技大學工業工程與管理研究所,碩士論文。
  28. 2. 林智廣 (1993) ,長期血液透析病患生活品質的評估,中山醫學院醫學研究所,碩士論文。
  29. 3. 洪冠予 (1997) ,實用透析治療手冊病案討論,文諍圖書出版社,頁275-283,台北。
  30. 4. 胡國華 (1997) ,模糊理論在血液透析之應用:透析療效綜合指標評估出行之建立,中原大學醫學工程研究所,碩士論文。
  31. 7. 黃秋錦 (1995) ,透析治療在台灣-1995年透析評估,中華民國腎臟醫學會雜誌,頁71-83。
  32. 8. 黃綺薇、吳彥英、劉素瑛 (1994) ,血液透析患者對秘方使用情形之調查,腎臟與透析第6 卷3 期,228~231。
  33. 9. 章樂琦、曾碧萊、李蕙蓉、唐威莉、胡玉卿等 (1998) ,腎臟疾病營養與膳療學,P433-446。
  34. 11. 陳玲慧 (2003) ,模糊血液透析品質指標評估輔助系統-以區域醫院血液透析資料庫為例,國立雲林科技大學工業工程與管理研究所,碩士論文。
  35. 12. 張軒研 (1996) ,血液透析病患生活品質及其影響因素之探討,國立陽明大學衛生福利研究所,碩士論文。
  36. 13. 透析手冊 (1998) ,長年出版社,頁14~24,高雄。
  37. 14. 項正川、蕭文衍、李文俊、郭泰松 (1994) ,血液透析病人營養失調問題之探討,腎臟與透析第6 卷1 期,頁67~74。
  38. 16. 劉文雄 (1994) ,血液透析醫療品質改善之初步臨床評估,中原大學醫學工程研究所,碩士論文。
  39. 17. 蔡信宏、陳建平 (2002) ,中西醫會診-腎衰竭,書泉出版社,台北市。
  40. 18. 蔡蕙如 (2006) ,建構血液透析過程低血壓之預測模型,國立雲林科技大學工業工程與管理研究所,碩士論文。
  41. 20. 譚柱光、黃東波編著 (1994) ,人工腎臟,力大圖書有限公司,台北市。
  42. 二、英文部分
  43. 21. Aamodt, A., Plaza, E. (1994), “Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches”, Artificial Intelligence Communications, Vol. 7:1, pp. 39-59.
  44. 26. Eberhart, R.C. & Shi, Y. (1998), “Comparison between Genetic Algorithms and Particle Swarm Optimization,” Proceedings of the Seventh annual Conference on Evolutionary Programming, Springer Verlag, pp. 611-618.
  45. 28. Fritz, H. Grupe (1993), “Case-based Reasoning Applying Past Experience to New Problems”, Information Systems Management, pp. 77-80.
  46. 31. Ignatavicius, D. D. and Bayne, M. V. (1991), Medical-Surgical Nursing: A nursing approach. Philadelphia: W. B. Saunders.
  47. 32. Igor, J., J. Mylopoulos, J. Glasgow, H. Shapiro, and R. F. Casper (1998), “Case-Based Reasoning in IVF: Prediction and Knowledge Mining,” Artificial Intelligence in Medicine, pp.1-14.
  48. 34. Jeffrey C. F. , James F. G. , Nancy C. A. , Mary s.T.,Paul D. L. (2000), “Within-center correlation in dialysis adequacy”. Journal of Clinical Epidemiology, 53, pp. 79-85.
  49. 35. Kennedy, J. and Eberhart, R.C. (1995), “Particle Swarm Optimization”, In proceedings of IEEE International Conference on Neural Networks,Vol. IV, pp. 1942-1948.
  50. 38. King, B. (1997). Preserving renal function, RN, 60(8), pp. 34-40.
  51. 39. Kolodner, J. (1993) ,”Case-based Reasoning” Morgan Kaufmann Publishers, Inc. ,San Mateo
  52. 41. Leake, D. B. (1996), “Case-Based Reasoning: experiences, lessons, and future directions, MIT Press”, Massachusetts Institute of Technology, Cambridge, Massachusetts and London, England.
  53. 45. Munoz, A., H., J. A. Hendler, and D. W. Aha. (1999), “Conversational Case-based Planning,” Review of Applied Expert Systems, 5, pp.163-174.
  54. 46. Ralph Barletta (1991),“An Introduction to Case-Based Reasoning”, AI Expert, pp. 43-49.
  55. 49. Schank, R. (1982), Dynamic Memory: A Theory of Learning in Computers and People, New York: Cambridge University Press.
  56. 50. Shi, Y. H., and Eberhart, R. C. (1998), “A modified particle swarm optimizer”, In IEEE World Congress on Computational Intelligence, IEEE Press.
  57. 51. Shi, Y. H., and Eberhart, R. C. (1998), “Parameter selection in particle swarm optimization”, In Evolutionary Programming VII (Berlin, 1998), pp. 591–600.
  58. 52. Slade, S. (1991), “Case-Based Reasoning:A Research Paradigm”, AI Magazine, 4(1), pp. 42-55.
  59. 54. Symth, B. (1999), “Constructing Competent Case based Reasoners: Theories, Tools and Techniques”, Proceedings of the Workshop on Automating the Construction of Case Based Reasoners, Stockholm, Sweden.
  60. 58. Zhenya, H., Chengjian, W., Luxi, Y., Xiqi, G., Susu, Y., Eberhart, R.C. and Shi, Y. (1998), “Extracting rules from fuzzy neural networks by particle swarm optimization”, 1998 IEEE International Conference on Evolutionary Computation, Vol. I, pp. 74–77.