Distributed Representation of Healthcare Text Through Qualitative and Quantitative Analysis

Many healthcare-related applications use pretrained embeddings, but these are often trained over general corpus which is mostly downstreamed to certain particular application. One problem noticed among such embeddings is that these are not efficient across various health text applications and even less number of research describe evaluation of these embedding for health domain. In this paper, distributional embedding model is performed to acquire a word representation on data crawled from Journal of Medical Case Reports. This distributed embedding model is analyzed qualitatively and quantitatively over crawled corpus. Qualitative evaluation is employed by cosine similarity on different categories and is visually represented. Quantitative evaluation performed with parts of speech tagging and entity recognition. The embedding model attained a cross-validation accuracy of 91.70% in parts of speech tagging for GENIA corpus and ensured 83% accuracy in the entity recognition of i2b2 clinical data.