A New Time Series Analysis Method and its Application in Analysis of Large Medical Databases

In recent years, more and more studies have focused on the large medical databases. Traditional statistical approaches and time series analysis methods are frequently used, but they have some limitations. Therefore, to develop an advanced time series analysis method is required. In this thesis, we develop the Fourier-Gaussian decomposition method and show that it can decompose a signal into a finite and small number of components. Fourier-Gaussian decomposition can extract different components with the same frequency from a signal, which is not available in other methods. Furthermore, we apply Fourier-Gaussian decomposition to analyze several diseases in Taiwan’s National Health Insurance Research Database (NHIRD) and the out of hospital cardiac arrest (OHCA) database. Finally, we get some interesting findings. We find special patterns in allergic rhinitis visits, asthma visits, and AMI visits. Allergic rhinitis visits contained one-year period and peaked in March and November; asthma visits peaked in April and November; AMI visits peaked in Spring Begins, Summer Begins and Winter Begins. Besides, we find that circulatory system diseases visits and digestive system diseases visits have the same pattern. The number of patients decreased rapidly at Vernal Equinox, Grain in Ear and Winter Solstice. In OHCA database, we find that the number of non-traumatic OHCA patients increased rapidly in winter and slightly in summer. The survival rate of non-traumatic OHCA patients increased in spring and autumn, which is reverse to the number of non-traumatic OHCA patients.