A deep learning way for disease name representation and normalization

Abstract

Disease name normalization aims at mapping various disease names to standardized disease vocabulary entries. Disease names have such a wide variation that dictionary lookup method couldn’t get a high accuracy on this task. Dnorm is the first machine learning approach for this task. It is not robust enough due to strong dependence on training dataset. In this article, we propose a deep learning way for disease name representation and normalization. Representations of composing words can be learned from large unlabelled literature corpus. Rich semantic and syntactic properties of disease names are encoded in the representations during the process. With the new way of representations for disease names, a higher accuracy is achieved in the normalization task.