| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 100 === 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.
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