Title

應用人工智慧於醫療資源利用率分析與探討-以股骨轉子間骨折手術為例

Translated Titles

A Study of Applying Artificial Intelligence to the Medical Resource Utilization - A Case of Intertrochanteric Fracture of Femur Surgery

Authors

邱慶宗

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

全民健保實施以來,因支付制度的不適用、醫療資源分配不當及病患不當用藥習慣等原因,導致健保當局財務日趨惡化。如何針對以上種種可能導致財務問題的原因,作出適當的處置與改善,為目前健保當局極需思量的問題。 台灣自1993年正式邁入高齡化社會,人體隨著年齡的增長而日趨老化虛弱,各種直接或間接的疾病症狀因而產生,致使老年人口的健保診治費用成為健保費用支出的大宗。老年人常因骨質疏鬆、骨強度降低、骨脆性增加的情況,稍不小心發生意外而骨折,當中又以髖部骨折為常見問題。髖部骨折是老年醫療照護的重要議題。本研究以髖部骨折中最常見之股骨轉子間骨折為主要研究對象。 老年人常患有糖尿病、高血壓及心臟病等疾病,而這些疾病皆可能會影響股骨轉子間骨折診治,使得醫療費用較一般病患來的高。本研究希望能藉由病患臨床資料以醫療費用與住院天數為指標,分別以案例式推理及類神經網路建立預估模型,並分別評估模型績效,最後再與健保當局現今實施之醫療資源配置實例比較,探討股骨轉子間骨折手術的醫療資源分配與利用率。 本研究應用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,預測住院天數部分,神經網路之均方根誤差為0.0518;案例式推理在誤差為一天時,準確率可達92.89%。預測醫療費用部分,案例式推理在誤差為2000元,率可達83.89%。

English Abstract

Ever since the implementation of National Health Insurance, the unsuitable on payment system, medical resource allocation and the medicine used, resulted in the aggravation of financial. How to handle and improve the above mentioned problems become an important issue for the authorities. As the age growth people getting weakness and ageing, also the direct or indirect disease arise, result in the tremendous diagnosis expense of health insurance for the elders. It is often for the elders to fracture because of osteoporosis, bone strength decrease and bone brittle increase. Intertrochanteric fracture of femur will be applied in this research. The elders often suffer from diseases such as the diabetes、high blood pressure and heart disease, and these diseases may influence the intertrochanteric fracture of femur treatment. This research plans to apply important factors that influence the intertrochanteric fracture of femur then based on the index of total medical expenditure and length of stay to establish the prediction model with case-based reasoning and neural network techniques, the performance of the model will be evaluated and compared with the real case of the Bureau of National Health Insurance to analyze the medical resource allocation and utilization of the intertrochanteric fracture of femur. In this study, we propose Neural Network and Case-Based Reasoning for assistance in medical resource planning. Results of prediction for length of stay showed that the classification of the RMSE of Neural Network is at 0.0518, and the accuracy with absolute tolerance at one day of Case-Based Reasoning reached 92.89%. And the results of expense, the accuracy with absolute tolerance at 2,000 dollars of Case-Based Reasoning reached 92.89%.

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. 2.田榮雯,2000,以FPGA實現倒傳遞類神經網路並應用於肌電圖分類,中原大學,碩士論文。
    連結:
  2. 7.吳肖琪,林麗嬋,藍忠孚,吳義勇,1998,“全民健保實施後急性病床住院病患超長住院情形之分析”,中華公共衛生雜誌,17卷,2期,頁139~147。
    連結:
  3. 8.邱建勳,黃世學,胡宗明,陳彥宇,李友專,王昱豐,2006,“預測血液透析病患放射免疫量測完整副甲狀腺素之目標範圍:人工智慧臨床應用”,核子醫學雜誌,19卷,3期,頁149~159。
    連結:
  4. 9.邱建勳,林暐棟,李友專,王昱豐,2005,“類神經網路模型預測連續性可攜式腹膜透析之尿毒症病患之完整副甲狀腺素”,核子醫學雜誌,18卷,3期,頁135~141。
    連結:
  5. 10.林淑萍,李奇學,呂陽樞,許玲女,2006,“比較邏輯迴歸模式與類神經網路模式對內科加護病房存活率之預測”,The Journal of Nursing Research,14卷,4期,頁306~314。
    連結:
  6. 12.祝道松,2004,“醫院實施臨床路徑對住院日數、醫療費用及醫療照護品質影響之研究-以人工髖關節置換手術為例”,健康管理學刊,2卷,1期,頁21~36。
    連結:
  7. 20.許明暉,李友專,邱文達,顏如娟,2005,“以類神經元網路預測中重度頭部外傷病患之預後”,北市醫學雜誌,2卷,3期,頁272~277。
    連結:
  8. 24.陳一傑,1998,應用模糊理論於多專家案例式推理之研究,元智大學,碩士論文。
    連結:
  9. 26.陳九五,黃明賢,蔡瑞熊,1994,“影響老人住院日數因素之初探”,The Kaohsiung Journal of Medical Sciences,10卷,12期,頁675~682。
    連結:
  10. 28.莊逸洲,黃崇哲,鄭明智,2003,“台灣醫院總額支付制度運作模式的初步探討”,醫務管理期刊,4卷,3期,頁1~16。
    連結:
  11. 33.黃淑俐,丁冠玉,黃元惠,2006,“全靜脈營養與重大腹部手術病患術後住院日數的相關因子探討”,台灣營養學會雜誌,31卷,3期,頁87~94。
    連結:
  12. 36.楊錦洲,陳百盛,2005,“應用類神經網路於顧客群之分類分析”,管理與系統,12卷,3期,頁43~65。
    連結:
  13. 41.蔡宗憲,李治綱,魏健宏,2006,“短期列車旅運需求之類神經網路預測模式建構與評估”,運輸計劃季刊,35卷,4期,頁475~505。
    連結:
  14. 42.蔡智勇,薛義誠,2005,“應用倒傳遞類神經網路預測台灣勞動市場人力需求”,中華管理學報,6卷,2期,頁1~14。
    連結:
  15. 45.鄭國棟,1991,住院日數影響因素之探討-以某醫學中心十種常見疾病為例,高雄醫學院,碩士論文。
    連結:
  16. 49.簡麗年,朱慧凡,劉見祥,鐘國彪,曹昭懿,吳義勇,吳肖琪,2003,“醫院、醫師手術量與醫療品質之相關性探討─以全股(髖)關節置換為例”,台灣衛誌,22卷,2期,頁118~126。
    連結:
  17. 50.魏敏雄,2005,手術量與醫療品質及利用之相關性探討—以全膝關節置換術為例,高雄醫學大學,碩士論文。
    連結:
  18. 51.羅健銘,2004,住院癌末病患照護型態對住院醫療費用與住院天數之影響,國立台北護理學院,碩士論文。
    連結:
  19. 53.蘇朝墩,唐麗英,柳進明,1997,“結合時間序列與類神經網路建構可靠度成長之預測模式研究”,中華民國工業工程學報,14卷,4期,頁419~430。
    連結:
  20. 55.饒玲瑜,2005,以Tw-DRGs分類探討全民健保住院日,疾病嚴重度及醫療費用之相關性,亞洲大學,碩士論文。
    連結:
  21. 2.Bert A. Mobley, Renee Leasure, Lynda Davidson, 1995, “Artificial neural network predictions of lengths of stay on a post-coronary care unit” , Administrative Issues, Vol. 24, pp.251~256.
    連結:
  22. 3.Boiardo R, Munoz E, Mulloy K, et al., 1990, “Economies of scale, physician volume for orthopedic surgical patients and the DRG prospective payment system” , Orthopedics, Vol. 13, pp.39~44.
    連結:
  23. 4.Burns L R, Wholey D R, 1991, “The effects of patient, hospital and physician characteristics on length of stay and mortality” , Med Care, Vol. 29, pp.251~71.
    連結:
  24. 5.Elizabeth G., Violante A., and Francisco C., 1998 , “Using neural networks for differential diagnosis for Alzheimer disease and Vascular dementia” , Expert Systems with Applications, Vol. 14, pp.219~225.
    連結:
  25. 6.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, Vol. 26, pp. 601~613.
    連結:
  26. 8.Gullberg B, Johnell O, Kanis J A, 1997, “World-wide projections for hip frature” , Osteopors, Vol. 7, No.5, pp.407~413.
    連結:
  27. 9.Harnib J W, Tang D G, Gordon T A, et al., 1999, “Hosptial volume can serve as a surrogate for surgeon volume for achieving excellent outcomes in colorectal resection” , Annals of Surgery, Vol. 230, pp.404~413.
    連結:
  28. 10.Hayashi Yoichi, Setiono Rudy, 2002, “Combining neural network predictions for medical diagnosis”, Computers in Biology and Medicine, Vol. 32, pp. 237~246.
    連結:
  29. 11.Hiroshi H., Yasuyuki O., Hitomi N., Seigo T., Hiroyuki T. and Hiroki M., 1996, “Application of neural network to the interpretation of laboratory data for the diagnosis of two forms of chronic active hepatitis” , International Hepatology Communications, Vol. 5, pp.160~165.
    連結:
  30. 12.Houghton A, 1994, “Variation in outcome of surgical procedures” , British J Surg, Vol. 81, pp.653~660.
    連結:
  31. 13.J. Hunt, 1997, “Case based diagnosis and repair of software faults” , Expert System, Vol. 14, No.1, pp.15~23.
    連結:
  32. 14.Jemsen J S, Wilberger J E, Fox P. P. Unstable trochanteric fractures, 1980, “A comparative analysis of four methods of internal fixation” , Acta Orthop Scand, Vol. 51, pp.949~951.
    連結:
  33. 15.Kannus P, Parkkari J, Sievanen H, et al, 1996, “Epidemiology of hip fractures” , Bone, Vol. 18, pp.57~63.
    連結:
  34. 16.Kenneth J. Ottenbacher, Richard T. Linn, Pamela M. Smith, Sandra B. Illig, Melodee Mancuso, Carl V. Granger, 2004, “Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture” , Annals of Epidemiology, Vol. 14, pp.551~559.
    連結:
  35. 19.Lee J. S., Xon Y. X., 1996, “A customer service process innovation using the integration of data base and case base” , Expert Systems with Applications, Vol. 11, No.2, pp.543~552.
    連結:
  36. 20.Lieberman M D, Kilburn H, Lindsey M, et al., 1995, “Relation of periopeative deaths to hospital volume among patients undergoing pancreatic resection for malignancy” , Annals of Surgery, Vol. 222, pp.638~645.
    連結:
  37. 21.Lippmann R. P, 1987, “An introduction to computing with neural nets” , IEEE ASSP Magaine, pp.4~22.
    連結:
  38. 22.Luft H S, Bunker J P, Enthoven A C, 1979, “Should operations be regionalized The empirical relation between surgical volume and mortality” , N Engl J Med, Vol. 301, pp.1364~1369.
    連結:
  39. 23.Mechitov A. I., Moshkovich H. M., Olson D. L., Killingsworth B., 1995, “Knowledge Acquisition Tool for Case-Based Reasoning Systems” , Expert Systems with Applications, Vol. 9, No.2, pp.201~212.
    連結:
  40. 24.Mehdi M., O. Owrang, 1998, “Case Discovery in Cased-Based Reasoning” , Information Systems Management, pp.74~78, Winter.
    連結:
  41. 25.Mohamed Ibnkahla, 2000, “Applications of neural networks to digital communications- a survey”, Signal Processing, Vol. 80, pp. 1185~1215.
    連結:
  42. 27.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, Vol. 74, pp. 299~306.
    連結:
  43. 28.Rumelhart D., Hinton G., Williams R., 1986, “LearningInternal Representations by Error Propagation” , Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Vol. 1, pp.318~362.
    連結:
  44. 29.Schank R. C., Abelson R. P., Scripts, Plans, 1977, Goals and Understanding, Lawrence Erlbaum Associates, Hillsdate N.J..
    連結:
  45. 30.Shu-Hsien Liao, 2005, “Expert system methodologies and applications-a decade review from 1995 to 2004” , Expert systems with Applications, Vol. 28, pp.93~103.
    連結:
  46. 32.Soohoo N F, Zingmond D S, Lieberman J R, Ko C Y, 2006, “Primary total knee arthroplasty in California 1991 to 2001: does hospital volume affect outcomes” , The Journal of Arthroplasty, Vol. 21, pp.199~205.
    連結:
  47. 33.Vincent K R, Vincent H K, Lee L W, Alfano A P, 2006, “Outcomes in total knee arthroplasty patients after inpatient rehabilitation: influence of age and gender” , Am J Phys Med Rehabil, Vol. 85, pp.482~489.
    連結:
  48. 34.Werbos P. J., 1974, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science, Harvard University, Master thesis.
    連結:
  49. 35.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, Vol. 31, pp. 415~420.
    連結:
  50. 中文部份
  51. 1.王詩翔,2005,替換調適模式之案例式推理於智慧型老人居家照護,國立政治大學,碩士論文。
  52. 3.朱佳雯,2004,案例式推理與類神經網路在心電圖診斷之應用研究,真理大學,碩士論文。
  53. 4.李鋒剛,倪志偉,王鍵,郜巒,黃玲,2006,“基於案例推理的腦血管病輔助診斷智慧系統的設計”,中華中醫藥學刊卷期,24卷,2期,頁260~262。
  54. 5.李得盛,2001,應用統計與類神經網路模式於監督式分類問題,國立交通大學,博士論文。
  55. 6.李奇學,1995,病人疾病嚴重程度與醫療資源耗用,醫療費用之相關研究-以某區域教學醫院加護病房為例,中國醫藥學院,碩士論文。
  56. 11.林昇獎,1996,案例式輔助工具在建築結構初步設計之應用,淡江大學,碩士論文。
  57. 13.洪維河,1990,病人住院日數與住院費用的關係-以DRG155為例,國立台灣大學,碩士論文。
  58. 14.施麗媛,2005,“老年骨折與處理”,老年醫學會期刊,51期。
  59. 15.施能仁,方南芳,1997,“以類神經網路建立台灣儲蓄互助社財務危機預警模式”,台灣經濟,247期,頁34~80。
  60. 16.施勵行,董大鈞,莊弘毅,1996,“類神經網路在中長期電力需求及負載預測之應用”,能源季刊,26卷,4期,頁59~75。
  61. 17.袁繼銓,2002,以類神經網路預測燒傷病患住院日之研究,國立中山大學,碩士論文。
  62. 18.梁定彭,1996,決策支援系統,松崗電腦圖書股份有限公司。
  63. 19.梁忠詔,2001,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,國立東華大學,碩士論文。
  64. 21.陳瑛瑛,2004,加護病房院內感染與抗藥性微生物之盛行率及其對住院天數和醫療成本之影響探討,國立陽明大學,博士論文。
  65. 22.陳章友,1999,類神經網路在醫學檢驗的應用─以肝病為例,國立交通大學,碩士論文。
  66. 23.陳重臣,方國定,1999,“以類分子神經系統對B型肝炎臨床資料作診斷預測、分析、交叉驗證”,管理與系統,6卷,4期,頁433~458。
  67. 25.陳朝順,曾燕明,卓明遠,1996,“應用類神經網路求解配電變電所負載預測及溫度敏感度分析”,中國工程學刊,19卷,2期,頁171~177。
  68. 27.梅玉成,1997,應用分散式類神經網路於財經資料庫之資料擷取與決策支援-以股票評等系統為例,國立台灣大學,碩士論文。
  69. 29.張文英,林恆德,黃榮輝,陳耀武,楊英魁,賴昭村,段建華,吳光超,賴政宏,1997,“開放式電力變壓器故障診斷專家系統之研究”,台電工程月刊,588期,頁34~57。
  70. 30.曹勝雄,曾國雄,江勁毅,1996,“傳統計量迴歸、模糊迴歸、GMDH、類神經網路四種方法在預測應用之比較-以國人赴港旅客需求之預測為例”,中國統計學報,34卷,2期,頁132~161。
  71. 31.郭斯傑,陳信夫,1997,“以類神經網路估算建築工程成本之比較研究”,建築學報,22期,頁81~94。
  72. 32.郭祥兆,李致寬,1995,“倒傳遞網路分析在財務危機預測之研究-以台灣地區股票上市公司為例”,基層金融,31期,頁77~95。
  73. 34.黃金生,施東河,劉建利,1996,“類神經網路在台灣人壽保險業股票風險溢酬預測的應用”,資訊管理學報,3卷,1期,頁63~80。
  74. 35.黃公怡,王福權,1984,“鵝頭釘治療股骨轉子間骨折的療效分析”,中華骨科雜誌,4期,頁349~353。
  75. 37.葉致昌,2005,“骨質疏鬆性骨折的手術治療”,嘉榮醫訊,89期。
  76. 38.葉怡成,2001,類神經網路模式應用與實作,儒林出版社,台北。
  77. 39.董正玫,1997,臨床路徑對醫療成本與住院天數之影響-以某區域醫院之闌尾切除術為例,國立陽明大學,碩士論文。
  78. 40.蔣廷芳,1994,“類神經網路股價預測系統”,企銀季刊,17卷,4期,頁40~49。
  79. 43.蔡素女,1991,住院日控制制度對住院日數影響之研究,國立台灣大學,碩士論文。
  80. 44.魯英,羅先正,1991,“例股骨粗隆間骨折治療分析”,骨與關節損傷雜誌,17卷,9期。
  81. 46.鄭錦翔,2004,臨床路徑應用於人工膝關節置換術之成效評估,國立中山大學,碩士論文。
  82. 47.盧世璧,王繼芳,朱盛修等,1987,“加壓滑動鵝頭釘治療股骨粗隆間骨折”,中華外科雜誌,頁63~78。
  83. 48.謝智新,2005,“老人的致命傷-髖部骨折”,高醫醫訊月刊,25卷,7期。
  84. 52.蘇木春、張孝德,2000,機械學習─類神經網路、模糊系統以及基因演算法,全華科技圖書股份有限公司。
  85. 54.蘇俊霖,2003,以案例式推理建構出院準備服務後續照護資訊系統,國立雲林科技大學,碩士論文。
  86. 英文部分
  87. 1.Aamodt A., Plaza E., 1994, Case-Based Reason:Foundational Issues, Methodological Variations, and System Approaches, IOS Press, Netherlands.
  88. 7.Fritz H. G , 1993, “Case-based Reasoning Applying Past Experience to New Problems” , Information Systems Management, pp.77~80.
  89. 17.Kolodner J. L., 1993, Case-Based Reasoning, Morgan Kaufmann Publishers, Inc, San Mateo.
  90. 18.Kreder H. J., Grosso P., Williams J. I., Jaglal S., Axcell T., Wal E. K., et al., 2003, “Provider volume and other predictors of outcome after total knee arthroplasty:a population study in Ontario” , Canadian Journal of Surgery, Vol. 46, pp.15~22.
  91. 26.Ralph, Barletta, 1991, “An Introduction to Case-Based Reasoning” , AI Expert, pp.43~49, August.
  92. 31.Slade S., 1991, “Case-Based Reasoning: A Research Paradigm” AI Magazine, Vol. 4, No. 1, pp. 42~55.