Entity Embedding in Medical Text Data
Cross-disciplinary Scholars in Science and Technology (CSST) project, Scalable Analytics Institute, University of California, Los Angeles, 2017
* Performed independent research on entity representation learning for the development of a medical case report query system, supervised by Prof. Yizhou Sun and Prof. Wei Wang * Constructed graphs for annotated domain-specific entities in clinical case reports and implemented an unsupervised graph embedding method with node2vec for the graph representation of case reports * Visualized result by projecting the 128-dimensional embedding of 2491 entities into 3D space using PCA * Evaluated result by calculating cosine similarity between medical terms and corresponding abbreviations, which outperformed Google's word2vec pre-trained word embedding