Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records

Abstract

The written notes in the Electronic Health Records contain a vast amount of information about patients. Implementing automated approaches for text classification tasks requires the automated methods to be well-interpretable, and topic models can be used for this goal as they can indicate what topics in a text are relevant to making a decision. We propose a new topic modeling algorithm, FLSA-E, and compare it with another state-of-the-art algorithm FLSA-W. In FLSA-E, topics are found by fuzzy clustering in a word embedding space. Since we use word embeddings as the basis for our clustering, we extend our evaluation with word-embeddings-based evaluation metrics. We find that different evaluation metrics favour different algorithms. Based on the results, there is evidence that FLSA-E has fewer outliers in its topics, a desirable property, given that within-topic words need to be semantically related.