Data Science: Similarity Least Squares (SLSTM) + physics + Statistical Design of Experiment (DOE)

            A New Paradigm for Analysis and Management of Complexity

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Examples:
HCV prognostic evaluations are presented for male and female patients. The important assumption is that calibration of the sources of patient data have been properly performed relative to the data used to develop the SLSTM model.

Consider a tale of two patients, a and b, at week 4 of treatment, a male and a female:

0)patient Profiles:
p sex age Genotype Fibrosis Act  PriorEndPt LogVLoad  LogALT LogVDropW4
a M   37   3	     2	     2      NA      5.088     1.845   3.088
b F   60   4	     3	     3      NR      6.326     2.376   0.585 
1)Convert all info to scaled values via scaling tables:
 sex   age   GT    Fib    Act   PriorEndPt LogVLoad  LogALT vLogDropW4
-0.07  37  -0.98  -0.52  0.07     -0.48	   5.088     1.845   3.088
-0.49  60   0.44   0.25  0.29      0.49	   6.326     2.376   0.585
2)Z-score (Zing) all info:
 sex     age    GT    Fib    Act   PriorEndPt LogVLoad  LogALT  vLogDropW4
 0.74  -1.08  -1.08  -0.44  0.36     -0.47     -1.908   -0.542   0.313
-1.34   1.34   1.04   0.68  0.66      1.32     -0.110    1.192  -1.459
3)Apply SLSTM model:
a V= -2.73 predicts SVR for the male
b V=  1.69 predicts NR for the female
 

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Copyright of James M Minor, July 4, 2004.
For problems or questions regarding this web contact email jmin007@yahoo.com
Last updated: June 20, 2013.