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研究生:蔡瑞瑩
研究生(外文):Jui-Ying Tsai
論文名稱:類流感就診人次預測之研究─以台中市類流感門診人次為例
論文名稱(外文):Forecasting Outpatient Visits for Influenza-like Illness—The Case of Taichung Influenza-like Illness Outpatient Visits
指導教授:蔡玫亭蔡玫亭引用關係
口試委員:郭佳瑋王建富
口試日期:2017-06-22
學位類別:碩士
校院名稱:國立中興大學
系所名稱:企業管理學系所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:56
中文關鍵詞:類流感就診人次需求預測時間序列分析多元迴歸分析
外文關鍵詞:Influenza-like-illness (ILI) visitsDemand forecastTime-series analysismultiple regression model
相關次數:
  • 被引用被引用:2
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  • 下載下載:104
  • 收藏至我的研究室書目清單書目收藏:1
類流感為變化性高,分布範圍遍布全球的流行病之一,每到疾病高峰期之際,就診人次的變化性便容易影響醫院的資源分配;然而迄今為止,類流感相關之研究較著重於整體國家之趨勢,故對醫院而言,其參考性有其限制在。本研究即鎖定我國類流感就診人次最高之縣市為預測對象,企望能提供該地區醫院在相關醫療資源準備(如藥品存貨量、人力資源分配)之參考。

在本研究所提出之預測模型中,為提升預測準確度,本研究嘗試加入不同層面之預測變數,包含時間序列、地區相關性、天氣因素、空氣汙染因素,以及預測之歷史資料長度五個層面,分析與目標地區類流感就診人次之關聯性,並以上述因素預測目標地區未來每週之類流感就診人次;本研究共使用四種預測模型,包含時間序列模型、地區相關性模型、多元迴歸模型以及混合式預測模型,並比較四者之預測能力。

本研究使用兩項政府開放資料為各項變數之資料來源,兩項資料來源皆為即時更新,故模型的預測值可隨官方資料持續更新。在分析結果中,本研究發現以目標地區加上特定縣市之就診人次為預測變數,可得到較佳的預測能力;又本研究發現,考慮了地區相關性、空氣汙染因素,以及特定歷史資料長度之預測模型,在全部模型中具有最佳的預測能力;而本研究尚針對各項預測變數之預測結果進行討論,認為我國氣候變化性較小,故對預測結果之解釋能力相對較低。以學者所提出之預測指標分類標準而言,本研究所提出之模型預測能力相當好,可提供目標地區醫院醫療資源分配之參考。
Influenza-like-illness (ILI) is one of the most changeable and wide-spread epidemic. During flu seasons, the variability of outpatient visits could affect medical resources in hospitals seriously. Nonetheless, up to date, most of the ILI-related researches are focused on the entire trend, making it difficult apply to regional demands. Hence, our study choose the city which involves the highest ILI outpatient number in Taiwan, trying to forecast the approaching outpatient visits. We anticipate that our study could benefit hospitals for medical resources preparation (e.g. pharmaceuticals inventory management and human resources assignment).

For the sake of improving forecast accuracy, our study proposes forecast models by adding various aspects of parameters, including time-series factor, geographical factor, weather factor, air pollution factor and the length of historical data. After analyzing the correlation between above factors and ILI outpatient visits of our target city, we forecast the approaching weekly outpatient visits in terms of the result, comparing forecast accuracy and then determine the best forecast model. We propose four kinds of forecast models, including time-series model, geographical correlation model, multiple regression model and mixed model.

The sources of data in our study comes from two terms of real-time open-data of authority; that is to said, the forecast value of our study could be updated with official data. We figure out that the forecast accuracy could be improved by adding geographical factor; moreover, our study concludes that the mixed model which involves geographical factor, air pollution factor and specific length of historical data could gain the best accuracy competence. Afterwards, we discuss about the result and conclude that the weak effect of weather factor is due to the less climate variability in Taiwan. To sum up, according to the standard of forecast index proposed from the scholar, our model forecasts well, which may be able to be taken into account by target city.
目錄
1. 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究方法 4
1.4 研究架構 5
2. 文獻探討 7
2.1 一般需求預測方法 7
2.2 醫療產業的預測 11
3. 類流感就診人次預測模型 24
3.1影響類流感趨勢之因素 24
3.2預測模型建立 26
3.3預測誤差之檢驗 30
4. 類流感門診人數預測結果與分析--以台中市為例 32
4.1資料說明 32
4.2時間序列預測結果與分析 36
4.3地區相關性之分析結果 37
4.4多元線性迴歸之分析結果 41
4.5混合式預測模型之分析結果 44
4.6模型變數之意涵 47
5. 結論與建議 49
5.1結論 49
5.2建議 50
參考文獻 52
參考書目

1.中文書目

白滌清(譯) (2010)。生產與作業管理:程序與供應鏈(原作者:LEE J. KRAJEWSKI, L.J., RITZMAN, L.P., and MALHOTRA, M.K.)。第九版。新北市:學銘圖書。

林茂文(2006)。時間數列分析與預測:管理與財經之應用。第三版。台北市:華泰文化。

林思宇(2016年3月3日)。流感肆虐 竟因接種疫苗人數太少。聯合財經網。檢索於2016年12月26日。

林俊宏(譯)(2013)。大數據(原作者:SCHöNBERGER, V.M. and CUKIER K.)。台北市:遠見天下文化。

空氣品質監測網。中華民國:行政院環境保護署。取自:http://taqm.epa.gov.tw/taqm/tw/EpbSiteListInMap.aspx。

政府資料開放平台。中華民國國家發展委員會。取自:http://data.gov.tw/node/9454。

紀志賢,曾詠淑,林錫璋,陳澤生,蔡良敏(1999)。以資料挖掘模式實施緊急救護量預測。11(4),337-341。

疾病管制署資料開放平台。中華民國:衛生福利部疾病管制署。取自:https://data.cdc.gov.tw/。

張保隆,陳文賢,蔣明晃,姜齊,盧昆宏,王瑞琛,黃明宮(2006)。生產管理。第三版。台北市:華泰文化。

郭明哲(1976)。預測方法:理論與實例。台北市:中興管理顧問公司。

褚志鵬,謝秀圓(2014)。不同藥品耗用類型預測暨庫存管理之研究。醫務管理期刊。15(1),55-72。

傳染病統計資料查詢系統。中華民國:衛生福利部疾病管制署。取自:http://nidss.cdc.gov.tw。

趙郁竹(2012年3月)。BIG DATA數字煉金。62-65。台北市:巨思文化-數位時代。檢索於2016年10月24日。

莊蕙嘉(2015年11月18日)。學名藥搶市 5年後全球用藥增30%。聯合晚報。檢索於2016年10月24日。http://money.udn.com/money/story/5599/1322104

簡聰海(2007)。生產與作業管理。新北市:新文京開發。

2.西文書目

ARAZ, O. M., BENTLY, D. & MUELLEMAN, R. L. 2014. Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska. American Journal of Emergency Medicine, 32(9), 1016-1023

BOUTSIOLI, Z. 2009. Measuring unexpected hospital demand the application of a univariate model to public hospitals in Greece. Hospital Topics, 87(4), 14-21

BOUTSIOLI, Z. 2013. Estimation of unpredictable hospital demand variations in two Piraeus public hospitals, Greece. Journal of Hospital Administration, 2(4), 126-137

BULTER D. 2013. When Google got flu wrong. Nature.com. 13/02/2013.

BURNS, L. R. 2002. The Health Care Value Chain: Producers, Purchasers, and Providers. Jossey-Bass Publications.

DOMNICH, A., PANATTO, D., SIGNORI, A., LUIGILAI, P., GASPARINI, R. & AMICIZIAL, D. 2015. Age-related differences in the accuracy of webquery-based predictions of influenza-like illness. Multidisciplinary Sciences, 10(5), 1-14

ESTELLES-AROLAS, E. and GONZALEZ-LADRON-DE-GUEVARA, F. 2012. Toward an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189-200

FOX, E. R., BIRT, A., JAMES, K. B., KOKKO, H., SALVERSON, S., SOFLIN, D. L. & HAWKINS, B. 2009. ASHP guidelines on managing drug product shortages in hospitals and health systems. American Journal of Health-System Pharmacy, 66, 1399-1406

GOOGLE INC. 2016. GOOGLE FLU TREND. http:// www.google.org/flutrends/about/

HANAUER, D. A. and RAMAKRISHNAN, N. 2013. Modeling temporal relationships in large scale clinical associations. Journal of the American Medical Informatics Association, 20(2), 332-341

HEIZER, J. and RENDER, B. 2014. Operations management: sustainability and supply chain management. 11th ed. Pearson Publications.

HOU, XW., ZHU, B., KANG, HQ. & GAO, JH. 2014. Analysis of seasonal ozone budget and spring ozone latitudinal gradient variation in the boundary layer of the Asia-Pacific region. Atmospheric Environment, 94, 734-741

HU, H., LI, J., PLANK, A., WANG, H. & DAGGARD, G. 2006. A comparative study of classification methods for microarray data analysis. In: Fifth Australasian Data Mining Conference. Sydney: Australian Computer Society, Inc, 33-37

JONATHAN, C., PRATHER, LOBACH, D. F., GOODWIN, L. K., R. N., HALES, J. W., HAGE, M. L., & HAMMOND, E. 1997. Medical data mining: knowledge discovery in a clinical data warehouse. Journal of the American Medical Informatics Association, 101-105

KHARE, R., GOOD, B. M., LEAMAN, R., SU, A. I. & LU Z. 2016. Crowdsourcing in biomedicine challenges and opportunities. Briefings in Bioinformatics, 17(1), 23-32

KIM, JH. and LEE, H. 2010. What Causes the Springtime Tropospheric Ozone Maximum over Northeast Asia. Advances in Atmospheric Sciences, 27(3), 543-551

KUTNER, M. H., NACHTSHEIM, C. J. & NETER J. 2008. Applied linear regression models, 4th ed. McGraw-Hill Education Publications.

LANDRY, S. and PHILIPPE, R. 2004. How logistics can service healthcare. Supply Chain Forum: An International Journal, 5(2), 24-30

LEWIS, E. B. 1982. Control of body segment differentiation in Drosophila by the bithorax gene complex, Embryonic Development, Part A: Genetics Aspects. Springer Science & Business Media Publications. 269-288

MENDENHALL, W. and SINCICH, T. 2012. Second course in statistics, A: regression analysis. 7th ed. Boston: Pearson Publications.

NIKOLOPOULOS, K., BUXTON, S., KHAMMASH, M. & STERN, P. 2016. Forecasting branded and generic pharmaceuticals. International Journal of Forecasting, 32, 344-357

SAEDI, S., KUNDAKCIOGLU, O. E. & HENRY, A. C. 2016. Mitigating the impact of drug shortages for a healthcare facility: An inventory management approach. European Journal of Operational Research, 251, 107-123.

SUPACHAI NAKAPAN, NITIN KUMAR TRIPATHI, TARAVUDH TIPDECHO & MARC SOURIS. 2012. Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand. International Journal of Environmental Research and Public Health, 9, 3824-3842.

TANDBERG, D., TIBBETTS, J. & SKLAR, D. P. 1998. Time series forecasts of ambulance run volume. American Journal of Emergency Medicine, 16(3), 232-237

VAN NOORT, S. P., AGUAS, R., BALLESTEROS, S. & GOMES, MGM. 2012. The role of weather on the relation between influenza and influenza-like illness. Journal of Theoretical Biology, 298, 131-137

VIJAYAN, V. V. and ANJALI C. 2015. Decision support systems for predicting diabetes mellitus –A review. In: 2015 Global Conference on Communication Technologies. Thuckalay: IEEE Xplore, 98-103

XU, Z., HU, W., WILLIAMS, G., CLEMENTS, A. C. A., KAN, H. & TONG, S. 2013. Air pollution, temperature and pediatric influenza in Brisbane, Australia. Environment International, 59, 384-388

YANG, H., KUNDAKCIOGLU, OE. & ZENG, D. 2015. Healthcare data analytics. Information systems and E-business management, 13(4), 595-597

YOO, I., ALAFAIREET, P., MARINOV, M., PENA-HERNANDEZ, K., GOPIDI, R., CHANG, JF. & HUA, L. 2012. Data mining in healthcare and biomedicine: a survey of the literature. Journal of Medical Systems, 36(4), 2431-2448
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