Wednesday March 09, 12pm-1pm, 1116-E Klaus
Med2Vec: Multi-layer Representation Learning for Medical Concepts
Advisor: Prof. Jimeng Sun
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics. However, in Electronic Health Records (EHR) the visit sequences of patients include multiple concepts (diagnosis, procedure, and medication codes) per visit. This structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within each visit. In this work, we propose Med2Vec, which not only learns distributed representations for both medical codes and visits from a large EHR dataset with over 3 million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec displays significant improvement in key medical applications compared to popular baselines such as Skip-gram, GloVe and stacked autoencoder, while providing clinically meaningful interpretation.
Edward Choi is a Ph.D. student in Computer Science, working with Prof. Jimeng Sun. Edward has a BS in CS from Seoul National University, and an MS in CS from KAIST, where he specialized in natural language processing. He has spent 4 years as member of the text mining team at ETRI (Electronics & Telecommunications Research Institute). Edward’s current interests are in applying representation learning methods in healthcare problems.