臺灣博碩士論文加值系統

溫度等氣候因素與疾病間的關係，在某些疾病已經被廣為了解，但是有些氣候與疾病間的關係尚未得到全面性的分析，尤其在二十四節氣方面對每年的疾病與氣候關係探討的研究還不夠充份。二十四節氣是中華文化特有的農業社會的曆法，與一年四季中的氣候變化相配合，使農作收穫最佳化。中醫古籍指出四季天氣的變動與身體協調失衡時會導致疾病發生，但文獻中沒有確切統計數據可具體的探討對每年二十四節氣中疾病與氣候的關係。
本研究針對國家衛生院之健保資料庫中2005至2010年的門診資料，與中央氣象局6年內氣溫氣壓等資料，將公曆中十二月份對應二十四節氣後，進行資料探勘。最後以交叉相關係數呈現氣候-節氣共病之關係，並進一步嘗試以現代理論解釋中醫在二十四節氣時間尺度下氣候對疾病的影響。
除了以年平均趨勢研究初步觀察各疾病的節氣趨勢外，本研究亦使用希爾伯特-黃轉換法中的經驗模態解析(Empirical Mode Decomposition, EMD)與整體經驗模式分析(Ensemble Empirical Mode Decomposition, EEMD)將統計之人數解析成數種內部模態函數(Intrinsic Mode Function)一一探討各疾病在二十四節氣下的趨勢變化，以及互相比較各疾病來探討其中之共病性，同時也對整體經驗模態分析實驗數種參數來選擇對該疾病的理想解。而與氣候的關係亦為本論文探討方向之一。
經由以上方法解析後可得到數組結果，除了未分解前的結果以外，使用EMD/EEMD演算法可將資料中的雜訊濾除，可取得較平滑的年週期波動。同時從疾病資料分解出的波動變化，使得更多的相關性被找出並進一步作其他的研究探討之。

The relationships between temperature and other climatic factors and diseases have been widely understood in some relationships between climatic factors and diseases but not been comprehensive enough in the other certain relationships, especially in the twenty-four solar terms time-scale. “Solar Terms” in Chinese culture is the specific calendar in conjunction with the seasonal climate in a year and can create best harvest. In traditional Chinese medicine the climate changes of four seasons makes human body imbalance and cause disease, but there are not enough literatures to present exact statistics specific in the relationship between disease and climate on the annual twenty-four solar terms.
In this study we used the data from outpatient records during years from 2005 to 2010 in National Health Insurance Research Database (NHIRD) and climatic factors including temperature and atmospheric pressure during the same years in Central Weather Bureau. Data in Gregorian calendar time scale would be transformed into twenty-four solar terms and mined. We also used cross-correlation coefficients to present the relationships between climates and diseases, and tried to explain how climatic factors impact diseases in the time scale of the solar terms on the view of traditional Chinese medicine by the modern theory.
In addition to the observations to the average annual trend of diseases, this study also used Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) in Hilbert-Huang Transform to decompose many Internal Mode Functions (Intrinsic Mode Function), exploring the trend of diseases in the time scale of solar terms, as well as comparing diseases to explore the comorbidity. We also compared many parameters to select the ideal solution to the disease data decomposed by EEMD. The trends of climate data and the relationship with diseases were also explored by the aforementioned methods
We obtained many results after these experiments. In addition to non-decomposed statistic results, we also found that EMD/EEMD algorithm can filter noises from the data and get smoother cyclical functions. While the more decomposition of the volatility of disease information but also makes more relevant to be searched out, and for the other study. As more functions decomposed, the more relationships would be found and could be studied more.

口試委員會審定書 #
誌謝 i
中文摘要 ii
Abstract iii
目錄 v
圖目錄 vii
表目錄 viii
Chapter 1 緒論 1
Chapter 2 文獻回顧 5
2.1 健保資料庫 5
2.1.1 健保資料庫之優缺點 5
2.1.2 健保資料庫之內容與疾病取用 7
2.2 歷年國內外時間序列相關分析 8
2.2.1 疾病年週期性研究 8
2.2.2 氣候週期性研究 9
2.2.3 時間序列分析方法 10
2.3 經驗模態分析(Empirical Mode Decomposition) 10
2.4 整體經驗模態分析(Ensemble Empirical Mode Decomposition, EEMD) 12
Chapter 3 資料收集與轉換 15
3.1 門診資料選取 15
3.1.1 疾病選取 16
3.1.2 資料格式轉換 18
3.1.3 數據整理及索引化 20
3.2 節氣氣候資料 22
Chapter 4 節氣趨勢統計與評估 25
4.1 資料統計方法 25
4.1.1 趨勢評估 25
4.1.2 資料標準化 26
4.1.3 交叉相關係數(Cross-correlation Function) 27
4.2 資料結果呈現 27
4.2.1 節氣趨勢呈現 27
4.3 疾病之平均值與標準差 34
4.4 疾病間數值直接比較 35
4.5 疾病與氣候數值直接比較 38
Chapter 5 EMD/EEMD之解析 40
5.1 演算法使用參數 40
5.1.1 同參數比對與跨參數比對 42
5.2 資料呈現 43
5.2.1 疾病統計 43
5.2.2 演算法 43
5.2.3 參數查找 44
5.2.4 圖表呈現 44
5.3 EMD 之結果 44
5.4 EEMD之結果 49
5.5 研究限制 55
Chapter 6 結論與未來展望 56
參考文獻 58
附錄一、使用疾病ICD-9病碼 I
附錄二、使用疾病之年齡與性別人數分布 III
附錄三、二十四節氣代號對照表與日期定義 IV

1.Fu, T.-c., A review on time series data mining. Engineering Applications of Artificial Intelligence, 2011. 24: p. 164-181.
2.Hata, T., et al., The seasonal variation of blood pressure in patients with essential hypertension. Clinical and experimental hypertension. Part A, Theory and practice, 1982. 4(3): p. 341-54.
3.Giaconi, S., et al., Seasonal Influences on Blood Pressure in High Normal to Mild Hypertensive Range. Hypertension, 1989. 14: p. 22-27.
4.YY, A.-T., A.-H. JM, and A.-R. EA., Seasonality of hypertension. J Clin Hypertens (Greenwich), 2008. 2: p. 125-129.
5.Brennan, P.J., et al., Seasonal variation in arterial blood pressure. BRITISH MEDICAL JOURNAL, 1982. 285(1982): p. 919-923.
6.Aberg, N., Birth season variation in asthma and allergic rhinitis. Clinical and Experimental Allergy, 1989. 19(6): p. 643-648.
7.Moller, C., et al., Pollen immunotherapy reduces the development of asthma in children with seasonal rhinoconjunctivitis (the PAT-study). Journal of Allergy and Clinical Immunology, 2002. 109(2): p. 251-256.
8.Targonski, P.V., V.W. Persky, and V. Ramekrishnan, Effect of environmental molds on risk of death from asthma during the pollen season. Journal of Allergy and Clinical Immunology, 1995. 95(5): p. 955-961.
9.Low, R., in Acupuncture: Technique for successful point selection2001, Butterworth-Heinemann Oxford. p. 1-14.
10.Wu, Y. and W. Fischer, Practical Therapeutics of Traditional Chinese Medicines1997: Paradigm Publications.
11.Chen, F.-p., et al., Use frequency of traditional Chinese medicine in Taiwan. BMC Health Services Research, 2007. 11: p. 1-11.
12.Lukman, S., Y. He, and S.-C. Hui, Computational methods for Traditional Chinese Medicine: a survey. Computer methods and programs in biomedicine, 2007. 88(3): p. 283-94.
13.吳秀美、徐勝一（1999）。二十四節氣在台灣－「大暑」及「大寒」之探討。跨世紀海峽兩岸地理學術研討會，臺北市，1-20。
14.Cheng, Q., Y.A.N. Zhongwei, and F.U. Congbin, Climatic changes in the Twenty-four Solar Terms during 1960 – 2008. Climatic Changes, 2012. 57(2): p. 276-286.
15.交通部中央氣象局（2011）。臺灣24節氣與氣候---1981~2010資料統計。臺北市：交通部中央氣象局。，1-205
16.Yang, A.C., et al., Do Seasons Have an Influence on the Incidence of Depression ? The Use of an Internet Search Engine Query Data as a Proxy of Human Affect. 2010. 5(10): p. 1-7.
17.Keogh, E. and S. Kasetty, On the Need for Time Series Data Mining Benchmarks : A Survey and Empirical Demonstration, in SIGKDD2002, ACM: Edmonton, Alberta, Canada. p. 23-26.
18.Huang, N.E., et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998. 454(1971): p. 903-995.
19.Huang, N.E. and Z. Wu, A review on Hilbert-Huang transform: method and its application to geophysical studies. Reviews of Geophysics, 2008. 46: p. 1-23.
20.Chang, N.-F., et al., On-line Empirical Mode Decomposition Biomedical Microprocessor for Hilbert Huang Transform. 2012: p. 420-423.
21.Wu, Z. and N.E. Huang, Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method. Advances in Adaptive Data Analysis, 2009. 1(1): p. 1-41.
22.Hsiao, F.-y., et al., Using Taiwan ’ s National Health Insurance Research Databases for Pharmacoepidemiology Research. 2007. 15(2): p. 99-108.
23.Yoo, I., et al., Data Mining in Healthcare and Biomedicine: A Survey of the Literature. Journal of medical systems, 2011.
24.Boari, B., et al., Circadian rhythms and cardiovascular diseases: clinical perspectives. Recenti Prog Med., 2006. 97(12): p. 727-732.
25.Manfredini, R., et al., Chronobiological patterns of onset of acute cerebrovascular diseases. Thromb Res., 1997. 88(6): p. 451-463.
26.Peckova, M., et al., Weelky and seasonal variation in the incidence of cardiac arrests. Am Heart J., 1999. 137(2): p. 512-515.
27.Moineddin, R., et al., Seasonality of primary care utilization for respiratory diseases in Ontario: a time-series analysis. BMC health services research, 2008. 8: p. 160-160.
28.Pudpong, N. and S. Hajat, High temperature effects on out-patient visits and hospital admissions in Chiang Mai, Thailand. Sci Total Environ, 2011. 409(24): p. 5260-7.
29.Cleveland, R.B., et al., STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics, 1990. 6(1): p. 3-73.
30.Huang, M.-C.W.N.E., Biomedical Data Processing Using HHT: A Review, 2009.
31.Wu, Z., et al., On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci U S A, 2007. 104(38): p. 14889-94.
32.Zhu, S.-M., et al., Analysing the Similarity of Proteins Based on a New Approach to Empirical Mode Decomposition, in iCBBE2010. p. 1-4.
33.燕海霞等（2011）。基于不同白噪聲幅值的總体平均經驗模態分解法分析中醫脈象的研究。生物醫學工程學雜誌，28(1)，22-26。
34.Kim, D. and H.-s. Oh, EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum. The R Journal, 2009. 1(1): p. 40-46.
35.BS, J., F. P, and I. DD, Are symptoms of anxiety and depression risk factors for hypertension? Longitudinal evidence from the National Health and Nutrition Examination Survey I Epidemiologic Follow-up Study. Archives of Family Medicine, 1997. 6(1): p. 43-49.
36.Grossman, E. and F.H. Messerli, Hypertension and Diabetes. Clinical, Metabolic and Inflammatory, 2008. 45: p. 82-106.
37.Howarth, E. and M.S. Hoffman, A multidimensional approach to the relationship between mood and weather. British Journal of Psychology, 1984. 75(1): p. 15-23.