Notes
A. A Note on Design and Optimization of Statistical Quality Control
Statistical quality control (QC) is essential for testing whether a process conforms to quality specifications or is out of control.
Generally, algebraic methods are unsuitable for the optimization of statistical QC, and enumerative methods can be cumbersome. However bioinspired techniques such as genetic algorithms (GAs) and artificial neural networks (NNs) offer attractive alternatives.
B. A Note on Reliability and Risk Based Optimization of Statistical Quality Control
Optimizing statistical QC of an analytical process can be approached as a probabilistic risk assessment problem involving the reliability analysis of the analytical system and the estimation of risk caused by measurement uncertainty.
C. A Note on Medical Diagnosis
Medical diagnosis involves identifying the unique characteristics of a disease through abduction, deduction, and induction. It can be threshold-based, assessed using diagnostic accuracy measures, or employ purely or empirical Bayesian approaches.
D. A Note on Uncertainty of Diagnostic Measures
Estimating and evaluating the uncertainty of diagnostic measures is critical in medical diagnosis and for defining analytical performance specifications.
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