Bayesian decision making based on measurements containing errors.
The paper presents a Bayesian approach to the construction of diagnostic and prognostic algorithms, based on the use of several diagnostic factors (tests) in combination. With this approach, the simultaneous use of dichotomous, discrete, categoric, and continuous factors is easy and the resulting algorithms are more efficient than those of other known methods. Moreover, each factor value that is available for use is assumed to represent an estimate (the result of an imperfect measurement) of some (unknown) true value. The proposed method of accounting for measurement errors is advantageous as regards efficiency for users of the algorithm (physicians) if the accuracy of the factor-measurement techniques at their disposal differs from that of the constructor of the algorithm (the scientist). The approach is illustrated by an example, and possible error models and methods of collecting statistical data are discussed.
Philosophov
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D000465:Algorithms; D001499:Bayes Theorem; D015982:Bias; D003625:Data Collection; D003663:Decision Trees; D003933:Diagnosis; D005163:Factor Analysis, Statistical; D006801:Humans; D016013:Likelihood Functions; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D016896:Treatment Outcome