Title

應用人工智慧於醫療資源之輔助規劃研究-以人工全膝關節置換術為例

Translated Titles

Using Artificial Intelligence for Assistance in Medical Resource Planning - A Case of Total Knee Arthroplasty Surgery

Authors

賴瑋諭

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

在眾多的老年常見的疾病中,關節炎佔有相當高的罹患比率,而膝關節的退化多少都會影響老人的生活。行動不便不但會造成了生理上的障礙,對於老人心理的衝擊影響甚鉅。根據健保申報資料統計顯示,台灣地區接受膝關節置換術從1998年到2003年約成長了29.9%,其利用率高且醫療費用成本高,佔健保相當份量的支出比例。 本研究的目的乃是要建構一個輔助臨床的評估系統。就醫院觀點而言,初期住院日評估可以協助醫師事先建立診療標準流程,在有限的醫療資源與人力情況下如何合理分配與評估並可作為後續出院照顧計畫之安排;就病患與家屬觀點而言,可減少病患對自己住院期間長短不確定性與減輕病患家屬精神壓力的心理調適。 本研究分別應用三種人工智慧技術(決策樹、類神經網路與案例式推理)以作為探討醫療資源分配之工具。研究結果顯示,決策樹正確分類率為78.53%;類神經網路之均方根誤差為0.0436;案例式推理在誤差為一天時,準確率可達79.21%。總結而言,本研究結果已顯示此系統的實際可行性,並提供適切結果。

English Abstract

Arthritis is the most common disease among the old people in Taiwan. Arthrosis of the knee caused tremendous inconvenience to the elders’ life. In addition, the disability not only impact old people physically but also mentally. According to the statistics released by Bureau of National Health Insurance, people who undertook the surgery of total knee arthroplasty have grown 29.9% from 1998 to 2003. It is a huge portion of the medical budget. In this thesis, the computer aided clinical evaluation system is expected to help the doctors to make a better decision. A standard procedure can be built up as doctors’ reference. Furthermore, it can work as reference for planning of aftercare under the limited medical resource and human resource. Finally, the uncertainty and anxiety of the patient and their family can be reduced. In this work, we propose the following three different methods of artificial intelligence technology for assistance in medical resource planning. It contained Decision Tree, Neural Network and Case-Based Reasoning. Results of this study showed that the classification of Decision Tree is as large as 78.53%, the RMSE of Neural Network is at 0.0436, and the accuracy with absolute tolerance at one day of Case-Based Reasoning reached 79.21%. To conclude, this study have demonstrated that these new systems can be practically implemented and provide adequate results.

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. 【3】 江琇琴,2001,論病例計酬醫師績效模式之建立及其在臨床路徑變異控制之應用,台北醫學大學,碩士論文。
    連結:
  2. 【7】 蔡智政,2002,應用CART決策樹與資料視覺技術於低良率晶圓成因探討,元智大學,碩士論文。
    連結:
  3. 【8】 戴慶玲,2003,南區某醫學中心人工關節置換術之預防性抗生素使用評估,高雄醫學大學,碩士論文。
    連結:
  4. 【9】 杜素珍,2003,個案管理之效益評估-以全膝關節置換術為例,台北醫學大學,碩士論文。
    連結:
  5. 【11】 魏敏雄,2005,手術量與醫療品質及服務利用相關性探討-以全膝人工關節置換術為例,高雄醫學大學,碩士論文。
    連結:
  6. 吳肖琪,1998,“全民健保實施後住院病患復健治療利用分析”,中華衛誌,17(1),19-27頁。
    連結:
  7. 【19】 盧瑞芬,李佳琳,莊逸洲,1999,“全民健康保險重大傷病患者住院醫療資源使用”,中華民國公共衛生雜誌,18 (4),283-292頁。
    連結:
  8. 【20】 林昭宏,2000, “影響腦中風患者復健治療住院天數長短的因素”,物理治療,25(2),64-74頁。
    連結:
  9. 【22】 李梅琛,賴裕和,陳美伶,劉淑娟,2001,“病人疼痛性質與疼痛信念對手術後使用止痛藥物之影響”,護理雜誌,48(1),49-57頁。
    連結:
  10. 【24】 周守民,顏妙芬,2002,“資訊時代中的臨床路徑發展”,台灣醫學,6(2), 251-255頁。
    連結:
  11. 【25】 林瓊珠,姚吟蓮,廖素滿,莊淑娟,林金燕,林碧珠,2002,“探討不同給藥方式對脊椎手術病患疼痛控制之成效”,榮總護理,19(1),21-34頁。
    連結:
  12. 【26】 簡麗年,朱慧凡,劉見祥,鍾國彪,曹昭懿,吳義勇,2003,“醫院、醫師手術量與醫療品質之關聯性探討-以全股(髖)關節置換為例”,台灣衛誌,22(2),118-126頁。
    連結:
  13. 【27】 石崇良,2003,“醫療錯誤之流行病學”,台灣醫學雜誌,8(4),510-520頁。
    連結:
  14. 【28】 黃昱瞳,翁心惠,楊長興,許玉君,2004,“醫師服務量對極低出生體重新生兒醫療費用的影響分析”,台灣衛誌,23(6),462-468頁。
    連結:
  15. 【29】 吳仁和,黃竫芬,黃彥結,辛信興,2004,“全膝關節置換術之併發症/合併症篩選研究”,中山管理評論,93-117頁。
    連結:
  16. 【30】 賴允亮,2004,“台灣之安寧緩和醫療”,台灣醫學,8(5),653-656頁。
    連結:
  17. 【31】 朱基銘,何子銘,盧瑜芬,許家瑋,白健佑,白璐,周雨青,孫建安,林金定,楊燦,Thomas Wetter,2006,“運用三種資料探勘方法預測子宮頸癌存活情形之比較”,台灣家醫誌,16(3),192-163頁。
    連結:
  18. 【2】 Alonso Fernando, Caraça-Valente Juan P. , González Angel L., Monte César, 2002, “Combining expert knowledge and data mining in a medical diagnosis domain”, Expert Systems with Applications, 23(4), pp. 367-375.
    連結:
  19. 【3】 Bert A. Mobley, Renee Leasure, Lynda Davidson, 1995, “Artificial neural network predictions of lengths of stay on a post-coronary care unit”, Administrative Issues, 24, pp. 251-256.
    連結:
  20. 【5】 Bayam Evrim, Liebowitz Jay, Agresti William, 2005, “Older drivers and accidents: a meta analysis and data mining application on traffic accident data”, Expert Systems with Applications, 29, pp. 598-629.
    連結:
  21. 【6】 Charlson ME, Pompei P, Ales KL, Mackenzie CR, 1987, “A new method of classifying prognostic comorbidity in longitudinal studies: development and validation”, Journal of Chronic Diseases , 40(5), pp. 373-383.
    連結:
  22. 【7】 Chae Young M. , Kim Hye S. , Tark Kwan C. , Park Hyun J. , Ho Seung H., 2003, “Analysis of healthcare quality indicator using data mining and decision support system”, Expert Systems with Applications, 24, pp. 167-172.
    連結:
  23. 【8】 Chang Chun-Lang, Cheng Bor-Wen, Su Jiun-Lin, 2004, “Using case-based reasoning to establish a continuing care information system of discharge planning”, Expert Systems with Applications, 26, pp. 601-613.
    連結:
  24. 【9】 Chang Chun-Lang, 2005, “Using case-based reasoning to diagnostic screening of children with developmental delay”, Expert Systems with Applications, 28, pp. 237-247.
    連結:
  25. 【10】 Chao-Ton Su, Chien-Hsin Yang, Kuang-Hsu, Wen-Ko Chiu, 2006, “Data mining for the diagnosis of type II diabetes from three- dimensional body surface anthropometrical scanning data”, Computers and Mathematics with Applications, 51, pp. 1075-1092.
    連結:
  26. 【11】 Deyo RA, Cherkin DC, Ciol MA, 1992, “Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases”, Journal of Clinical Epidemiology, 45(6), pp. 613-619.
    連結:
  27. 【12】 Ennett Colleen M., Frize Monique, Charette Elaine, 2004, “Improvement and automation of artificial neural networks to estimate medical outcomes”, Medical Engineering and Physics, 26, pp. 321-328.
    連結:
  28. 【13】 Elias Zintzarasa, Maria Baib, Christos Douligerisc, Axel Kowaldd, Panayiotis Kanavarose, 2007, “A tree-based decision rule for identifying profile groups of cases without predefined classes: application in diffuse large B-cell lymphomas
    連結:
  29. ”, Computers in Biology and Medicine, 37, pp. 637-641.
    連結:
  30. 【14】 Hayashi Yoichi, Setiono Rudy, 2002, “Combining neural network predictions for medical diagnosis”, Computers in Biology and Medicine, 32, pp. 237-246.
    連結:
  31. 【15】 Hervey SL, Purves HR, Guller U, Toth AP, Vail TP, Pietrobon R, 2003, “Provider volume of total knee arthroplasties and patient outcomes in the HCUP- nationwide inpatient sample”, The Journal of Bone and Joint Surgery American, 85, pp. 1775-1783.
    連結:
  32. 【16】 Hartge Florian, Wetter Thomas, Haefeli Walter E., 2006, “A similarity measure for case based reasoning modeling with temporal abstraction based on cross-correlation”, Computer Methods and Programs in Biomedicine, 81, pp. 41-48.
    連結:
  33. 【17】 Huang Mu-Jung, Chen Mu-Yen and Lee Show-Chin, 2007, “Integrating data mining with case- based reasoning for chronic diseases prognosis and diagnosis”, Expert Systems with Applications, 32, pp. 856-867.
    連結:
  34. 【18】 Jacques Demongeot, Gilles Virone, Florence Duchêne, Gila Benchetrit, Thierry Hervé, Norbert Noury, Vincent Rialle, 2002, “Multi-sensors acquisition, data fusion, knowledge mining and alarm triggering in health smart homes for elderly people”, Comptes Rendus Biologies, 325(6), pp. 673-682.
    連結:
  35. 【19】 Kenneth J. Ottenbacher, Pam M. Smith, Sandra B. Illig, Richard T. Linn, Roger C. Fiedler, Carl V. Granger, 2001, “Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke”, Journal of Clinical Epidemiology, 54, pp. 1159-1165.
    連結:
  36. 【21】 Katz JN, Barrett J, Mahomed NN, Baron JA, Wright RJ, Losina E, 2004, “Association between hospital and surgeon procedure volume and the outcomes of total knee replacement”, The Journal of Bone and Joint Surgery American, 86, pp. 1909-1916.
    連結:
  37. 【22】 Kenneth J. Ottenbacher, Richard T. Linn, Pamela M. Smith, Sandra B. Illig, Melodee Mancus, Carl V. Granger, 2004, “Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture”, Annals of Epidemiology, 14, pp. 551-559.
    連結:
  38. 【23】 Kusiak Andrew, Dixon Bradley, Shah Shital, 2005, “Predicting survival time for kidney dialysis patients: a data mining approach”, Computers in Biology and Medicine, 35, pp. 311-327.
    連結:
  39. 【24】 Kusiak Andrew, Caldarone Christopher A., Kelleher Michael D., Lamb Fred S., Persoon Thomas J., Burns Alex, 2006, “Hypoplastic left heart syndrome: knowledge discovery with a data mining approach”, Computers in Biology and Medicine, 36, pp. 21-40.
    連結:
  40. 【25】 Linder Roland, Pöppl Siegfried J., 2003, “A new neural network approach classifies olfactory signals with high accuracy”, Food Quality and Preference, 14, pp. 435-440.
    連結:
  41. 【26】 Losina E, Plerhoples T, Fossel AH, Mahomed NN, Barrett J Creel AH, Wright EA, Katz JN, 2005, “Offering patients the opportunity to choose their hospital for total knee replacement: impact on satisfaction with the surgery”, Abbreviation: Arthritis Rheum, 53(5), pp. 646-652.
    連結:
  42. 【27】 Mohamed Ibnkahla, 2000, “Applications of neural networks to digital communications- a survey”, Signal Processing, 80, pp. 1185-1215.
    連結:
  43. 【28】 Montani Stefania, Bellazzi Riccardo, 2002, “Supporting decisions in medical applications: the knowledge management perspective”, International Journal of Medical Informatics, 68, pp. 79-90.
    連結:
  44. 【29】 Moore Jason H., Gilbert Joshua C., Tsai Chia-Ti, Chiang Fu-Tien, Holden Todd, Barney Nate, White Bill C., 2006, “A flexible computational framework for detecting, characterizing and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility”, Journal of Theoretical Biology, 241, pp. 252-261.
    連結:
  45. 【30】 Philbin EF, McCullough PA, Dec GW, Disalvo TG., 2001, “Length of stay and procedure utilization are the major determinants of hospital charges for heart failure”, Clin Cardiol, 24, pp. 56-62.
    連結:
  46. 【31】 Perner Petra, Perner Horst, Müller Bernd, 2002, “Mining knowledge for hep-2 cell image classification”, Artificial Intelligence in Medicine, 26, pp. 161-173.
    連結:
  47. Podgorelec Vili, Kokol Peter, Stiglic Milojka Molan, Heriêko Marjan, Rozman Ivan, 2005, “Knowledge discovery with classification rules in a cardiovascular dataset”, Computer Methods and Programs in Biomedicine, 80, pp. S39-S49.
    連結:
  48. 【34】 R. P. Lippmann, 1987, “An introduction to computing with neural nets”, IEEE ASSP Magazine, 4(2), pp. 4-22.
    連結:
  49. 【35】 Rossille Delphine, Laurent J. F., Burgun Anita, 2005, “Modelling a decision support system for oncology using rule-based and case-based reasoning methodologies”, International Journal of Medical Informatics, 74, pp. 299-306.
    連結:
  50. 【36】 Robert Steele, Steve M. Green, Michelle Gill, Victor Coba, Bismark Oh, 2006, “Clinical decision rules for secondary trauma triage: predictors of emergency operative management”, Annals of Emergency Medicine, 47(2), pp. 135-145.
    連結:
  51. Steven Walczak, Walter E. Pofahl, Ronald J. Scorpio, 2002, “A decision support toll for allocating hospital bed resources and determining required acuity of care”, Decision Support Systems, 34, pp. 445-456.
    連結:
  52. 【39】 Semenova Tatiana, 2004, “Discovering patterns of medical practice in large administrative health databases”, Data and Knowledge Engineering, 51, pp. 149-160.
    連結:
  53. 【40】 Shu-Hsien Liao, 2005, “Expert system methodologies and applications- a decade review from 1995 to 2004”, Expert Systems with Applications, 28, pp. 93-103.
    連結:
  54. 【41】 Szpurek D., Moszynski R., Smolen A., Sajdak S., 2005, “Artificial neural network computer prediction of ovarian malignancy in women with adnexal masses”, International Journal of Gynecology and Obstetrics, 89, pp. 108-113.
    連結:
  55. 【42】 Shu-Kay Ng, Geoffrey J. Mclachlan, Andy H. Lee, 2006, “An incremental em-based learning approach for on-line prediction of hospital resource utilization”, Artificial Intelligence in Medicine, 36, pp. 257-267.
    連結:
  56. 【43】 Siermala Markku, Juhola Martti, 2006, “Techniques for biased data distributions and variable classification with neural networks applied to otoneurological data”, Computer Methods and Programs in Biomedicine, 81, pp. 128-136.
    連結:
  57. 【44】 Soohoo NF, Zingmond DS, Lieberman JR, Ko CY, 2006, “Primary total knee arthroplasty in California 1991 to 2001: does hospital volume affect outcomes”, The Journal of Arthroplasty, 21(2), pp. 199-205.
    連結:
  58. 【45】 Soohoo NF, Lieberman JR, Ko CY, Zingmond DS, 2006, “Factors predicting complication rates following total knee replacement”, The Journal of Bone and Joint Surgery American, 88(3), pp. 480-485.
    連結:
  59. 【46】 T.G. Buchman, K. L. Kubos, A. J. Seidler, M. J. Siegforth, 1994, “A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit”, Critical Care Medicine, 22(5), pp. 750-762.
    連結:
  60. 【47】 Vincent KR, Vincent HK, Lee LW, Alfano AP, 2006, “Outcomes in total knee arthroplasty patients after inpatient rehabilitation: influence of age and gender”, American Journal of Physical Medicine Rehabilitation, 85(6), pp. 482-489.
    連結:
  61. 【48】 Xu Jie, Ge Haiyan, Zhou Xiaolin, Yan Jinglong, Chi Qiang, Zhang Zhipeng, 2005, “Prediction of vascular tissue engineering results with artificial neural networks”, Journal of Biomedical Informatics, 38, pp. 417-421.
    連結:
  62. 【49】 Yamamura Shigeo, Kawada Keiko, Takehira Rieko, Nishizawa Kenji, Katayuama Shirou, Hirano Masaaki, Momose Yasun, 2004, “Artificial neural network modeling to predict the plasma concentration of aminoglycosides in burn patients”, Biomedicine and Pharmacotherapy, pp. 239-244.
    連結:
  63. 【50】 Yeong Eng-Kean, Hsiao Tzu-Chien, Chiang Huihua Kenny, Lin Chii-Wann, 2005, “Prediction of burn healing time using artificial neural networks and reflectance spectrometer”, Burns, 31, pp. 415-420.
    連結:
  64. Hwang C. L., Yoon K., 1981, Multiple Attribute Decision Making: Method and Applications, Springer- Verlag, New York.
    連結:
  65. 【53】 Janet Kolodner, 1993, Case Base Reasoning, Morgan Kaufmann Publishers, San Francisco.
    連結:
  66. 【55】 Richard J. Roiger, Michael W. Geatz, 2003, Data mining- a tutorial- based primer, Pearson Education.
    連結:
  67. 【57】 Saaty T. L., 1980, The Analytic Hierarchy Process, McGraw- Hill, New York.
    連結:
  68. 中文文獻
  69. 【1】 楊昱琦,2000,醫院服務支付制度對住院醫療費用之影響,逢甲大學,碩士論文。
  70. 【2】 蔡詩怡,2000,應用模糊整合類神經網路於疾病診斷之研究-以肝病為例,交通大學,碩士論文。
  71. 【4】 陳記成,2001,供應鏈存貨模式在不確定性因素下的採購決策支援系統, 台北科技大學,碩士論文。
  72. 【5】 王淑美,2002,全膝關節置換術論病例酬醫療資源耗用分析-以北部某醫學中心為例,陽明大學,碩士論文。
  73. 【6】 梁忠詔,2002,影響復健科患者住院天數長短因素之探討-以花東某醫學中心為例,東華大學,碩士論文。
  74. 【10】 周世凱,2003,案例式推理應用於營建工程職業災害判決系統之研究,雲林科技大學,碩士論文。
  75. 【12】 曾維功,2005,心臟衰竭病患住院日數與預後關係之探討-以台灣中區某區域教學醫院為例,中國醫藥大學,碩士論文。
  76. 【13】 潘麗馡,2005,實施臨床路徑對甲狀腺切除術病患醫療資源耗用與醫療不良事件之影響,長榮大學,碩士論文。
  77. 【14】 魏敏雄,陳智賢,2006,“醫師手術量與醫療品質及服務利用關聯性-以全膝關節置換術為例”,台灣公共衛生學會暨台灣流行病學學會,台北。
  78. 【15】 鄧振源,曾國雄,1989,“層級分析法(AHP)的內涵特性與應用-上”,中國統計學報,27(6),5-22頁。
  79. 【16】 鄧振源,曾國雄,1989,“層級分析法(AHP)的內涵特性與應用-下”,中國統計學報,27(7),1-20頁。
  80. 【17】 陳威明,1997,“淺談人工關節置換術”,榮總人,12(11),9-11頁。
  81. 【18】
  82. 【21】 王雅如,2001,“住院日控制-中國醫藥學院附設醫院之經驗”,醫院雜誌,28(2),78頁。
  83. 【23】 沈希哲,2001,“我推動臨床路徑五年的經驗”,台灣醫界,44(6),37-38頁。
  84. 【32】 劉致和,2000,資料採礦(Data Mining)-燙傷住院病人資料庫可能的研究方向,中華民國兒童燙傷基金會-燙傷專業新知。
  85. 【33】 葉怡成,2001,類神經網路模式應用與實作,儒林出版。
  86. 【34】 中華民國醫院協會,2001,ICD-9-CM 2001年中英對照,台北。
  87. 【35】 尹相志,2006,SQL Server 2005資料採礦聖經,學貫行銷,台北。
  88. 【36】 林奇益,2006,“雙膝微創全膝關節置換手術X光實例”,敏盛醫院暨中心診所醫院,www.ortho-lin.url.tw。
  89. 英文文獻
  90. 【1】 Aamodt A., Plaza E., 1994, “Cased-based reasoning: foundational issues, methodological variations and system approaches”, AI Communications IOS Press, 7(1), pp. 39-59.
  91. 【4】 B. Wyns, L. Boullart, S. Sette, D. Baeten, I. Hoffman, F. De Keyser, 2004, “Prediction of arthritis using a modified Kohonen mapping and case based reasoning”, Engineering Applications of Artificial Intelligence, 17(2), pp. 205-211.
  92. 【20】 Kreder HJ, Grosso P, Williams JI, Jaglal S, Axcell T, Wal EK, Stephen DJ, 2003, “Provider volume and other predicators of outcome after total knee arthroplasty: a population study in Ontario”, Canadian Journal of Surgery, 46, pp. 15-22.
  93. 【32】
  94. 【33】 Rumelhart D. E., Hinton G. E., Williams R. J., 1986, “Learning internal representation by propagation errors” , Parallel Distributed Processing Cambridge, MA MIT Press.
  95. 【37】 Slade S., 1991, “Case-based reasoning: a research paradigm”, AI Magazine, 4(1), pp. 42-55.
  96. 【38】
  97. 【51】 Werbos P. J., 1974, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Harvard University Cambridge MA, Doctoral Dissertation.
  98. 【52】
  99. 【54】 Margaret H. Dunham, 2003, Data Mining: Introductory and Advanced Topics, Prentice Hall, New Jersey.
  100. 【56】 Roger Schank, 1982, Dynamic memory: a theory of learning in computers and people, Cambridge University Press, New York.
  101. 【58】 Peter Chapman (NCR), Randy Kerber(NCR), Julian Clinton (SPSS), Thomas Khabaza (SPSS), Colin Shearer (SPSS), Thomas Reinartz (Daimler Chrysler), Rudiger Wirth (Daimler Chrysler), 2000, CRISP-DM 1.0 Step-by-Step data mining guide, http://www.crisp-dm.org.