摘要
Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.
Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.
