TITLE:
Predict the Future Hospitalized Patients Number Based on Patient’s Temporal and Spatial Fluctuations Using a Hybrid ARIMA and Wavelet Transform Model
AUTHORS:
Shundong Lei
KEYWORDS:
Medical Resources, Data Mining, Multi-Scale, ARIMA, Wavelet Transform Spatial Distribution
JOURNAL NAME:
Journal of Geographic Information System,
Vol.9 No.4,
August
11,
2017
ABSTRACT:
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.