To clarify the characteristics of clinical states of liver disease, the principal component analysis, the multi-dimensional Auto-Regression method, the cross-correlation and the cross-covariance method were applied to time-seriesed clinical laboratory data of patients with liver disease by using a t
Simulation and prediction of clinical states of leukemia by the auto-regression model
β Scribed by H. Mori; S. Kitamura; T. Nagaoka; A. Takekawa; N. Yamaguchi
- Publisher
- Elsevier Science
- Year
- 1982
- Tongue
- English
- Weight
- 336 KB
- Volume
- 24
- Category
- Article
- ISSN
- 0378-4754
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β¦ Synopsis
The Auto-Regression method was applied to the modelling of leukemic states. The number of two kinds of normal cells and of a kind of abnormal cells in the peripheral blood were taken as output variable. The model obtained explained the trend of the normal cells in the prediction , while it could not satisfactorily follow the time course of abnormal leukemic cells. Therefore, the differential equation model based on the knowledge on the drug effect to the abnormal cells was constructed. The conbination of these two models seems to be feasible for describing the dynamics of leukemic states.
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The chain geometry of polystyrene (PS) and polyarylate (PAr) block copolymer was predicted by the simulation of the kinetics of the block-copolymerization route. The simulation model consisted of a combination of two models. In the first model, the kinetics of the free-radical polymerization of carb