2206A Student Center
Traditional pathology diagnostic process routinely relies on disease-specific diagnostic clues. We propose an informatics pipeline to identify and quantify additional diagnostic clues that, in addition to traditional disease-specific clues, can improve diagnostic outcomes and decrease the chance of diagnostic pitfalls. We used our PathEdEx whole-slide imaging platform to record user activities related to diagnosing a cancerous tissue slide along with the biological features that were noted in the tissue by the examining pathologist as relevant to the diagnosis. To identify and quantify additional diagnostic clues that can improve diagnosis, we extended association rule mining techniques to measure information gain of the additional diagnostic clues. To validate our findings, we computed Kullback-Leibler divergence that indicates information gain generated by additional diagnostic clues.