| 摘要 |
A multivariate diagnostic method based on optimizing diagnostic likelihood ratios through the effective use of multiple diagnostic tests is disclosed. The Neyman-Pearson Lemma provides a mathematical basis to produce optimal diagnostic results. The method can comprise identifying those tests optimal for inclusion in a diagnostic panel, weighting the result of each component test based on a multivariate algorithm described below, adjusting the algorithm's performance to satisfy predetermined specificity criteria, generating a likelihood ratio for a given patient's test results through said algorithm, providing a clinical algorithm that estimates the pretest probability of disease based on individual clinical signs and symptoms, combining the likelihood ratio and pretest probability of disease through Bayes' Theorem to generate a posttest probability of disease, interpreting that result as either positive or negative for disease based on a cutoff value, and treating a patient for disease if the posttest probability exceeds the cutoff value.
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