Creating a Semantic Graph from Medical Documents

Akash Kaul, Student, Washington University in St. Louis

Working with medical documents is often challenging due to the niche vocabulary and the sheer amount of information. One method of making the content stored in these texts more accessible is by finding the key words and phrases. This NLP technique is known as named-entity recognition, and it is extremely powerful for representing the key ideas of any text in an efficient and usable way. This session will show how to setup a simple NLP pipeline to extract key entities from medical texts. This extracted information will then be translated into a graph to model the relationships between articles. Finally, the logical structure of the graph will be used to uncover similarities between medical documents.

Download the session slides here.

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