Informatics Framework to Identify Consistent Diagnostic Techniques Pathologists diagnose diseases according to diagnostic clues (DCs) such as clinical features, morphological features, and expression of antigens on the surface of a cell population. The specific use of these complex DCs constitutes a diagnostic technique. However, interpretation of these DCs may not be the same by all pathologists, subtle differences could lead to misdiagnosis or diagnostic pitfalls. Given that certain DCs express similarly for different disease types, are interrelated, or missing it is challenging to analyze DCs in order to find consistent diagnostic techniques. To address that challenge, we discuss a framework based on Shannon information entropy, which groups cases that represent consistent diagnostic techniques. The analysis of DCs from 35 cases generated three groups. Evaluation studies show that the results are statistically significant. In conclusion, this framework can be useful for the analysis of sparse data and has the potential to discover groups of cases that represent consistent diagnostic heuristics.

A Review of the Application of Information Theory to Clinical Diagnostic Testing The fundamental information theory functions of entropy, relative entropy, and mutual information are directly applicable to clinical diagnostic testing. This is a consequence of the fact that an individualxe2x80x99s disease state and diagnostic test result are random variables. In this paper, we review the application of information theory to the quantification of diagnostic uncertainty, diagnostic information, and diagnostic test performance. An advantage of information theory functions over more established test performance measures is that they can be used when multiple disease states are under consideration as well as when the diagnostic test can yield multiple or continuous results. Since more than one diagnostic test is often required to help determine a patientxe2x80x99s disease state, we also discuss the application of the theory to situations in which more than one diagnostic test is used. The total diagnostic information provided by two or more tests can be partitioned into meaningful components.