Improve the trustwortiness of medical text interpretations

Abstract

Currently, how to make a concrete and correct disease prediction is a popular research trend. Researchers made more efforts to develop various models to provide interpretations of medical area, however, there is still lack of human understandable explanations provided due to the non-transparency structure of some machine learning and deep learning models. According to this work, there is one combined model application we would like to adopt. After comparison experiments of classification and interpretation, it is found the combination model can address the issues from the latest interpretation models, and try to improve the trustworthiness of medical text interpretations.