急性腹痛為急診常見的主訴之一，從最輕微的急性胃炎到會隨時致命的主動脈剝離都會以腹痛來表現。然而，腹部藏了眾多的器官，醫師必須從病史詢問、身體檢查、實驗室檢驗到影像檢查，將可能的原因逐一抽絲剝繭找出問題，明確地鑑別複雜腹腔內感染 (complicated intra-abdominal infection, cIAI) 與其他疾病。本研究以資料探勘對臨床數據進行分類與預測，應用商業智慧描述性分析急診複雜腹腔內感染之可能性，並預期將複雜且雜亂的數據注入專家知識，達到決策支援的目的。
此研究回溯性收集2017年1月至6月在台灣南部某醫學中心急診就診之成年患者，且接受腹部電腦斷層檢查的資料，從電子病歷提取患者生命徵象、身體檢查的初始表現及檢驗數值，經由線上即時分析處理 (On-Line Analytical Processing, OLAP)，以多個維度來剖析急診病患數據，運用資料探勘軟體以決策樹、隨機森林及梯度提升等探勘方法建立模型，了解資料間的關聯性，並找出急診患者之複雜腹腔內感染的重要特徵，最終以視覺化圖表呈現與說明分析之結果。
結果顯示變數中以腹痛徵狀及檢驗項目中C反應性蛋白 (C-reactive protein, CRP)、丙胺酸轉胺酶 (glutamic pyruvic transaminase, GPT) 的數值差異對診斷cIAI有顯著影響。針對cIAI患者數據，收縮壓 (systolic blood pressure, SBP)、白血球 (white blood cell, WBC)、中性球 (segmented neutrophil, Seg)、GPT及CRP皆較正常值高的傾向，其中針對SBP建議觀察患者之長時間的血壓變化，而疼痛感較為特異的類別，例如：右下腹反彈痛、右下腹壓痛、右下腹轉移痛、及右上腹痛，有比較高的機率診斷為cIAI。透過梯度提升模型建立模型，達到85.71%的準確率，確立關鍵變數為檢驗項目中的白血球及血紅蛋白 (hemoglobin)，讓醫療人員在臨床執行醫療照護時，能注意患者指標。綜觀急診患cIAI的患者在執行電腦斷層檢查前，仍有許多防線能及早發現，以提醒醫療人員需更深入的了解，如此才能利用數據為臨床加值。

From acute gastritis to lethal aortic dissection, acute abdominal pain is a common presentation in the emergency department (ED). However, the abdomen contains many vital organs. Hence, emergency physicians have to differential diagnosis complicated intra-abdominal infection (cIAI) from other disease by means of taking the patients' medical history, requesting a physical examination, performing laboratory tests, and conducting imaging examinations, such as X-ray and computed tomography. This study practices data mining to predict and classify clinical data and applies business intelligence analytics to analyze cIAI in ED for the decision-making process.
The research retrospectively enrolled 1,628 patients who visited the ED of a tertiary teaching hospital in Southern Taiwan from January to June in 2017. The collected clinical data include patients’ vital signs, clinical symptoms, and laboratory data. Meanwhile, On-line analytical processing (OLAP) technique was applied to analyze data with multiple dimensions. We also developed models via integrating decision trees, random forests, and gradient boosting methods to identify the correlation of data. And the research results were presented visually by charts to help effective data presentation.
These results suggest that the abdominal pain symptoms and the increments in the values of C-reactive protein (CRP) and glutamic pyruvic transaminase (GPT) have a significant impact on the diagnosis of cIAI. We disclose the values in cIAI patients of systolic blood pressure, white blood cell (WBC), segmented neutrophil, GPT and CRP are higher than normal, but it is more appropriate to observe the patients’ long-term blood pressure variability; patients with specific pain, such as RLQ rebound pain, RLQ tenderness, RLQ transfer pain, and RUQ pain have higher possibilities to be diagnosed as cIAI; the gradient boosting model demonstrates the highest prediction with the accuracy of 85.71% that shows the key variables are established as WBC and hemoglobin. On this basis, the concept of patients suffering from cIAI still have many lines of defense that can be detected early before performing the CT examination.

中文文獻
蔡宏明、蔡秉諺（譯）（2011），SAS Enterprise Guide實用工具書（原作者：Slaughter, S. J. & Delwiche, L. D.），初版，梅霖文化事業有限公司，2011
曾淑峰、林志弘、翁玉麟（2012），資料探勘應用–以SAS Enterprise Miner為工具，初版，梅霖文化事業有限公司
閻雲（2017），個人化醫療：基因學於臨床應用之整合，台灣醫學會，取自http://www.fma.org.tw/2015/bio-1.html Accessed Jan 5, 2017.
衛生福利部全民健康保險會（2020），109 年全民健康保險醫療給付費用總額協商參考指標要覽，取自：https://www.nhi.gov.tw/Content_List.aspx?n=D529CAC4D8F8E77B&topn=23C660CAACAA159D

英文文獻
Ateş, E. C., Bostanci, E., & Güzel, M. S. (2020). Big data, data mining, machine learning, and deep learning concepts in crime data. Journal of Penal Law and Criminology, 8(2):293-31
Berry, M. W., Mohamed, A., & Yap, B. W. (2020). Supervised and Unsupervised Learning for Data Science. Springer Nature Switzerland AG., ISBN 978-3-030-22475-2
Breiman, L. (2001). Random Forests. Machine learning, 45(1):5-32
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. 1st Edition, Boca Raton, ISBN 978-1-315-13947-0
Cartwright, S. L. & Knudson, M. P. (2008). Evaluation of acute abdominal pain in adults. American Family Physician, 77(7)
Changpetch, P. & Reid, M. (2020). Data mining techniques: which one is your favorite? Journal of Education for Business, 96(3):143-148
Chaudhuri, S. & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1):65-74
Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8):88-98
Choi, R. Y., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., & Campbell, J. P. (2020). Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology, 9(14)
Claridge, J. A., Banerjee, A., Kelly, K. B., Leukhardt, W. H., Carter, J. W., Haridas, M., & Malangoni, M. A. (2014) Bacterial species-specific hospital mortality rate for intra-abdominal infections. Surg Infect (Larchmt)., 15(3):194-199
Codd, E. F., Codd, S. B., & Salley, C. T. (1993). Providing OLAP to user-analysts: an IT mandate., Technical Report, E. F. Codd & Associates, 1993.
De Simone, B., Coccolini, F., Catena1, F., Sartelli, M., Di Saverio, S., Catena, R. … Ansaloni, L. (2015) Benefits of WSES guidelines application for the management of intra-abdominal infections. World Journal of Emergency Surgery,10(18)
Dolmatova, E. V., Wang, K., Mandavilli, R., & Griendling, K. K. (2021). The effects of sepsis on endothelium and clinical implications. Cardiovasc Res., 117: 60-73
Eidizadeh, R., Salehzadeh, R., & Esfahani, A. C. (2017). Analysing the role of business intelligence, knowledge sharing and organisational innovation on gaining competitive advantage. Journal of Workplace Learning, 29(4):250-267
Elena, C. (2011) Business intelligence. Journal of Knowledge Management, Economics and Information Technology, 1(2):1-10
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3):37-54
Gandomi, A. & Haider, M. (2015) Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35:137-144
Gelot, S. & Nakhla, E. (2016) Intra-abdominal infections in adults. US Pharm., 41(4):5-12
Gulati, P., Sharma, A., & Gupta, M. (2016). Theoretical study of decision tree algorithms to identify pivotal factors for performance improvement: a review. International Journal of Computer Applications, 141(14)
Hajian-Tilaki, K. (2013). Receiver Operating Characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med., 4(2):627-635.
Hall, J. E. & Hall, M. E. (2020). Guyton and Hall Textbook of Medical Physiology. 14th edition, Elsevier, Inc., ISBN: 978-0-323-64003-9
Hamoud, A. K. & Obaid, T. A. S. (2014). Using OLAP with diseases registry warehouse for clinical decision support. International Journal of Computer Science and Mobile Computing, 3(4):39-49
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. 225, Wyman Street, Waltham, MA 92451, US, ISBN 978-0-123-81479-1
Hansen, K., Boyle, A., Holroyd, B., Phillips, G., Benger, J., Chartier, L. B., … Truesdale, M. (2020). Updated framework on quality and safety in emergency medicine. Emerg Med J, 37:437-442
Herland, M., Khoshgoftaar, T. M., & Wald, R. (2014). A review of data mining using big data in health informatics. Journal of Big Data, 1(2)
Herschel, R. T. & Jones, N. E. (2005). Knowledge management and business intelligence: the importance of integration. Journal of Knowledge Management, 9(4):45-55.
Hoffmann, C., Zak, M., Avery, L., & Brown, J. (2016) Treatment modalities and antimicrobial stewardship initiatives in the management of intra-abdominal infections. antibiotics (Basel), 5(1)
Hu, M., Li, W., Li, L., Houston, D., & Wu, J. (2016). Refining time-activity classification of human subjects using the global positioning system. PloS ONE, 11(2):e0148875
Hu, S. Y., Hsieh, M. S., Lin, M. Y., Hsu, C. Y., Lin, T. C., How, C. K., …Chang, Y. Z. (2016). Trends of CT utilisation in an emergency department in Taiwan: a 5-year retrospective study. BMJ Open., 6(6):e010973.
Jadidi, A., Mostafavi, M. A., Bédard, Y., Long, B., & Grenier, E. (2013). Using geospatial business intelligence paradigm to designa multidimensional conceptual model for efficient coastalerosion risk assessment. Journal of Coastal Conservation, 17:527-543
Jijo, B. T. & Abdulazeez, A. M. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(1):20-28
Johnson, J. M. & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(27)
Kass, G. V. (1980), An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2):119-127.
Katz, J. D., Mamyrova, G., Guzhva, O., & Furmark, L. (2010). Random forests classification analysis for the assessment of diagnostic skill. Am J Med Qual, 25(2):149-53
Kirmani, M. M. (2017) Dimensional modeling using star schema for data creation. Orient. J. Comp. Sci. & Technol., 10(4): 745-754
Kumaran, S. R., Othman, S. R. & Yusuf, L. M. (2016) Data mining approaches in business intelligence: postgraduate data analytic. Jurnal Teknologi (Sciences & Engineering), 78(8):75-79
Lewis, D. (2001). Data warehousing: Planning key to payoff. InternetWeek, 853, 52
Liu, S., Li, Y., She, F., Zhao, X., & Yao, Y. (2021). Predictive value of immune cell counts and neutrophil-to-lymphocyte ratio for 28-day mortality in patients with sepsis caused by intra-abdominal infection. Burns Trauma., 9:tkaa040.
Lopez, N., Kobayashi, L. & Coimbra, R. (2011) A comprehensive review of abdominal infections.World J Emerg Surg., 7
Ly, N. & McCaig, L. F. (2002). National hospital ambulatory medical care survey: 2000 emergency department summary. advance data from vital and health statistics. National Center for Health Statistics, 236
Marshall, J. & Innes, M. (2003) Intensive care unit management of intra-abdominal infection. Crit Care Med., 31:2228-2237.
Masmoudi, M., Jarboui, B., & Siarry, P. (2021). Artificial Intelligence and Data Mining in Healthcare, 1st edition, Springer, Cham, ISBN: 978-3-030-45239-1
Mazuski, J. E., Tessier, J. M., May, A. K., Sawyer, R. G., Nadler, E. P., Rosengart, M. R. … Prince, J. M. (2017) The surgical infection society revised guidelines on the management of intra-abdominal infection surgical infections. Surgical Infections, 18(1):1-76
Milanovic, M. & Stamenkovic, M. (2016). CHAID decision tree: methodological frame and application. Economic Themes, 54(4):563-586
Montravers, P., Gauzit, R., Muller, C., Marmuse, J. P., Fichelle, A., & Desmonts, J. M. (1996) Emergence of antibiotic-resistant bacteria in cases of peritonitis after intraabdominal surgery affects the efficacy of empirical antimicrobial therapy. Clin Infect Dis. 23(3):486-494
Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3):1314-1324
Natekin, A. & Knoll, A. (2013). Gradient boosting machines, a tutorial., Front. Neurorobot., 7(21)
Nivethithaa, K. K. & Vijayalakshmi, S. (2021). Survey on data mining techniques, process and algorithms. Journal of Physics: Conference Series, 1947, 012052
Nonaka, I. & Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York.
Pandey, N. R., Bian, Y., & Shou, S (2014). Significance of blood pressure variability in patients with sepsis. World J Emerg Med.; 5(1):42–47
Penny, K. I. & Atkinson, I. (2012). Approaches for dealing with missing data in health care studies. Journal of Clinical Nursing, 21(19):2722–2729
Pines, J. M. (2009). Trends in the rates of radiography use and important diagnoses in emergency department patients with abdominal pain. Medical Care, 47(7):782–786
Prasdika, P. & Sugiantoro, B. (2018). A review paper on big data and data mining concepts and techniques. International Journal on Informatics for Development, 7(1)
Quinlan, J. R. (1993). C4.5: Programming for machine learning. Morgan Kauffmann, 16:235-240
Quinlan, J.R. (1986). Induction of decision trees. Machine learning, 1:81-106
Ramageri, B. M. (2010). Data mining techniques and applications. Indian Journal of Computer Science and Engineering, 1(4):301-305
Richards, G., Rayward-Smith, V. J., Sönksen, P. H., Carey, S., & Weng, C. (2001). Data mining for indicators of early mortality in a database of clinical records. Artificial Intelligence in Medicine, 22(3):215-231
Saberi-Karimian, M., Khorasanchi, Z., Ghazizadeh, H., Tayefi, M., Saffar, S., Ferns, G. A., & Ghayour-Mobarhan, M. (2021). Potential value and impact of data mining and machine learning in clinical diagnostics. Critical Reviews in Clinical Laboratory Sciences, 58(4)
Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing and Management, 54(5):758-790
Sartelli, M.(2010) A focus on intra-abdominal infections. World J Emerg Surg., 5, 9.
Sartelli, M., Chichom-Mefire, A., Labricciosa, M. F., Hardcastle, T., Abu-Zidan, F. M., Adesunkanmi, A. K. … Catena, F. (2017) The management of intra-abdominal infections from a global perspective: 2017 WSES guidelines for management of intraabdominal infections. World Journal of Emergency Surgery.,12:29
SAS Help Center, (2017). SAS Enterprise Miner Analytics. access from: http://documentation.sas.com/doc/en/emcs/14.3/n0pejm83csbja4n1xueveo2uoujy.htm
SAS Taiwan, (2020). SAS Blogs, access from: https://blogs.sas.com/content/
Seliya, N., Khoshgoftaar, T. M. & Hulse, J. V. (2009). A study on the relationships of classifier performance metrics. 21st IEEE International Conference on Tools with Artificial Intelligence, 59-66.
Seltani, R., Aknin, N., Amjad, S., Chrayah, M., & El Kadiri, K. E. (2016). A collaborative process of decision making in the business context based on online questionnaires. International Journal of Advanced Computer Science and Applications, 7(7)
Sharda, R., Delen, D., & Turban, E. (2019). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th edition, Pearson Education (US), ISBN: 978-1-292-34155-2
Siguenza-Guzman, L., Saquicela, V., Avila-Ordóñez, E., Vandewalle, J., & Cattrysse, D. (2015). Literature review of data mining applications in academic libraries. The Journal of Academic Librarianship, 41(4):499-510.
Silva-Nunes, J. & Cardoso, T. (2019) Intra-abdominal infections: the role of different classifications on the selection of the best antibiotic treatment. BMC Infect Dis. 19(1):980
Solomkin, J. S., Mazuski, J. E., Bradley, J. S., Rodvold, K. A., Goldstein, E. J. C., Baron, E. J. … Bartlett, J. G. (2010) Diagnosis and Management of Complicated Intra-abdominal Infection in Adults and Children: Guidelines by the Surgical Infection Society and the Infectious Diseases Society of America. Clinical Infectious Diseases., 50:133-164
Spiegelhalter, D., Pearson, M., & Short, I. (2011). Visualizing uncertainty about the future., SCIENCE ,333 (9)
Stein, G., Chen, B., Wu, A. S., & Hua, K. A. (2005). Decision tree classifier for network intrusion detection with GA-based feature selection. ACM-SE 43: Proceedings of the 43rd annual Southeast regional conference – 2,March 2005 , 136-141
Sturkenboom, M. C., Goettsch, W. G., Picelli, G., Veld, B., Yin, D. D., de Jong, R. B., … Herings, R. M. C. (2005) Inappropriate initial treatment of secondary intra-abdominal infections leads to increased risk of clinical failure and costs. Br J Clin Pharmacol., 60(4):438-43
Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003). Random forest: a classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Sciences, 43(6):1947-1958
Tang Y., Sorenson, J., Lanspa, M., Grissom, C. K., Mathews, V. J., & Brown, S. M. (2017). Systolic blood pressure variability in patients with early severe sepsis or septic shock: a prospective cohort study. BMC Anesthesiology ,17:82
Vasilakis, C., El-Darzi, E. & Chountas, P. (2006). An OLAP-enabled software environment for modeling patient flow. 3rd International IEEE Conference Intelligent Systems., 261-266
Velmahos, G. C., Kamel, E., Berne, T. V., Yassa, N., Ramicone, E., Song, Z., & Demetriades, D. (1999) Abdominal Computed Tomography for the Diagnosis of Intra-abdominal Sepsis in Critically Injured Patients. Arch Surg., 134(8):831-838.
Wang, H. H. & Wang, S. (2008) A knowledge management approach to data mining process for business intelligence. Industrial Management & Data Systems, 108(5): 622-634.
Wang, H., Ma, C., & Zhou, L. (2009). A Brief Review of Machine Learning and its application. International Conference on Information Engineering and Computer Science (ICIECS), Dec 2009:1-4.
Wang, Y., Li, Y., Song, Y., Rong, X., & Zhang, S. (2017). Improvement of ID3 algorithm based on simplified information entropy and coordination degree. Algorithms, 10(4):124.
Watson, H. J., Goodhue, D. L., & Wixom, B. H. (2002). The benefits of data warehousing: why some organizations realize exceptional payoff. Information & Management, 39(6): 491-502
Weir, J. (1998). Data mining: Exploring the corporate asset. Information Systems Management, 15(4)
Wiharto, W., Kusnanto, H., & Herianto, H. (2016). Interpretation of clinical data based on C4.5 algorithm for the diagnosis of coronary heart disease. Healthcare Informatics Research, 22(3), 186.
Yoo, K., Yoo, H., Lee, J. M., Shukla, S. K., & Park, J. (2018). Classification and regression tree approach for prediction of potential hazards of urban airborne bacteria during asian dust events. Scientific Reports, 8(1).
Zhang, Z., Zhao, Y., Canes, A., Steinberg, D., & Lyashevska, O. (2019). Predictive analytics with gradient boosting in clinical medicine. Annals of Translational Medicine, 7(7):152
Zheng, G., Zhang, C. & Li, L. (2014) Bringing business intelligence to health information technology curriculum. Journal of Information Systems Education, 25(4)