Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19876
標題: 以法則式分類器預測手術時間
A Rule-Based Classifier for Operation Time Prediction
作者: 江淑如
Chiang, Shu-Ju
關鍵字: 開刀房
手術室排程
手術時間
預測模式
關聯規則探勘
出版社: 資訊科學與工程學系所
引用: [1]林怡君,運用模擬技術於手術室排程管理—以某醫學中心為例,國立臺灣大學醫療機構管理研究所碩士論文,2003。 [2]林重賢,手術時間預測模式建立,國立臺灣大學醫療機構管理研究所碩士論文,2002。 [3]張云濤, 龔玲編著, 資料探勘原理與技術:Data mining、Al、Algorithm, 五南, 2007。 [4]曾憲雄等著, 資料探勘:Data mining, 旗標, 2006。 [5]蔣安仁,開刀房利用最佳化之研究,國立中山大學醫務管理研究所碩士論文,2004。 [6]B. Cardoen, E. Demeulemeester and J. Belien, “Operating room planning and scheduling: A literature review”, European Journal of Operational Research,vol. 201,pp. 921-932,2010. [7]B. Cardoen, E. Demeulemeesterz and J. Belien, “Operating room planning and scheduling problems:A classification scheme”, European Journal of Operational Research, 2011. [8]S. I. Davies, “Machine learning at the operating room of the future: A comparison of machine learning techniques applied to operating room scheduling”, Master’s Thesis, Massachusetts Institute of Technology, 2004. [9]F. Dexter and L. O’Neill, “Letter to Editor: Previous research in operating room scheduling and staffing”, Health Care Management Science, 2010. [10]S. A. Erdogan and B. T. Denton, “Surgery Planning and Scheduling: A Literature Review”, Working Paper, 2009. [11]H. Jiawei and K. Micheline, “Data mining : concepts and techniques”, Morgan Kaufmann, 2006. [12]M. Lamiri , X. Xie, A. Dolgui and F. Grimaud, “A stochastic model for operating room planning with elective and emergency demand for surgery”, European Journal of Operational Research, vol. 185, pp. 1026-1037, 2008. [13]A. Macario, “Truth in Scheduling: Is It Possible to Accurately Predict How Long a Surgical Case Will Last? ” , Anesthesia & Analgesia, vol. 108,pp. 681-685, 2009. [14]K. Mehmed, “Data mining : concepts, models, methods, and algorithms”, Wiley-Interscience, 2003. [15]J. M. V. Oostrum , M. V. Houdenhoven, J. L. Hurink, E.W. Hans, G. Wullink and G. Kazemier, “A master surgical scheduling approach for cyclic scheduling in operating room departments”, OR spectrum, vol. 30, pp.355-374, 2008. [16]P. N. Tan, M. Steinbach and V. Kumar, “Introduction to Data Mining”, Addison-Wesley, 2006. [17]L. G. Vargas, J. H. May, W. Spangler, A. Stanciu, and D. P. Strum, “Operating Room Scheduling and Capacity Planning”, Anesthesia Informatics, pp. 361-392, 2009. [18]R. E. Wachtel and F. Dexter, “Influence of the Operating Room Schedule on Tardiness from Scheduled Start Times”, Anesthesia & Analgesia, vol. 108, pp. 1889-1901, 2009. [19]R. E. Wachtel and F. Dexter, “Reducing Tardiness from Scheduled Start Times by Making Adjustments to the Operating Room Schedule” , Anesthesia & Analgesia, vol. 108, pp. 1902-1909,2009. [20]I. H. Witten and E. Frank, “Data mining : practical machine learning tools and techniques”, Morgan Kaufman, 2005.
摘要: 近年來,醫院經營競爭激烈,加上全民健保的介入,使得開源不易,醫院管理者紛紛以節流為努力的目標。而手術室是醫院資源最為密集的地方:高科技醫療儀器與設備、各類醫療專業人員等都使得手術室無論在建造費用或作業成本上都非常昂貴。由於手術室集人力成本高及資本密集的特徵,管理者不得不重視其管理是否恰當,資源是否妥善利用,因此手術室管理成為醫院管理者關心的重點。如何使用資訊系統協助手術排程作業進行,對各大醫院而言均是一個重要的議題。而影響手術排程最重要的因素就是手術時間,一旦手術時間能夠被準確的預估,手術排程就能夠做最有效的規畫。 本研究採用中部某醫學中心手術相關資料作為訓練資料,提出了利用關聯規則作為法則分類器的方法,透過分析各關聯規則的支持度與可信度,找出預測強度最高的規則,作為預測的準則。本研究所提出的方法,對於主手術碼、次手術碼及手術醫師均為電子化資料的環境下,在資料取得上、與原手術室系統的結合上、以及與後續的應用上,都較為容易且不受限制。本方法可以在第一時間就整合在手術室系統的控管上,對於手術排程的控管上有即時性,而不需在事後經資料的統計分析再去擬定手術排程的調整,能達到最有效的控管,且與手術室系統的相容性高,可快速的導入應用中。以此為基礎,未來將可應用於開立手術通知單、病患動態電子看板與手術排程轉檔,解決手術排程的難題,以降低開刀房成本(ex:加班費…)、解決恢復室候床問題、並減少病患及家屬等待時間,提升病患滿意度,達到整體成本的降低。在無法擴大開源的情況下,起碼能達到節流的效果,以提升開刀房整體收益。
Hospital competition is fierce in recent years. One reason is because the implementation of the national health insurance that makes it more difficult to broaden sources of income. Reducing expenditure becomes a goal to achieve for the hospital administrator. Due to the high cost of human and equipments in the operation room, the operating room management focuses on maximizing operational efficiency at the facility, i.e. to maximize the number of surgical cases that can be done on a given day while minimizing the required resources and related costs. Nothing is more important than to first allocate the right amount of operating room time to each operation. If the operation room time can be estimated accurately, the operating room can be scheduled efficiently. In this study, the concept of association rule is adopted as a classification model. The prediction power of a classification rule is estimated by its support and confidence. The rules with the highest prediction power are used as the classification rules. The proposed rule-based classifier can be integrated with existing operating room management system.
URI: http://hdl.handle.net/11455/19876
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