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Word Embeddings for Clinical Systems
Author
Hathaitorn Rojnirun, Oluseye Bankole
Last Updated
há 5 anos
License
Creative Commons CC BY 4.0
Abstract
In this paper, we evaluate a baseline word embedding model for a set of clinical notes derived from patient records. For our baseline, we extract features for this embedding using the Word2Vec module from the gensim package. We also build two models, a word2vec skipgram model with negative sampling and a positive point-wise mutual information (PPMI) model by training on the processed clinical notes. Our evaluation shows that both the PPMI and the skipgram models show improved results for medically-related terms when compared with the baseline model. PPMI shows the best result out of all three models.
![Word Embeddings for Clinical Systems](https://writelatex.s3.amazonaws.com/published_ver/11331.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240727T120345Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240727/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=089430bff18e932b711116da3a241f16ffdfba6aa182acb81d1cc55065ae39b5)