Informatics Framework to Identify Consistent Diagnostic Techniques

Abstract

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.