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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Preprocessing clinical variables to be used in SLSTM model

  1. Calibration: since different lab sites tend to provide clinical measurements with site-specific biases, it is important to correct variables relative to the data used to train and validate the model. This is generally true of any model leveraging combinatorial relations among the variables.
  2. Scaling nominal's: conversion of variables with discrete levels (ie, nominal's) to variables with ordered scaled levels to improve statistical efficiency and leverage
  3. Zing: standardize all variables to z-scores, (x-mean)/stdDev
    z-scores improves model robustness.

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Copyright of James M Minor, July 4, 2004.
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Last updated: June 20, 2013.