![Word Embeddings for Clinical Systems](https://writelatex.s3.amazonaws.com/published_ver/11331.jpeg?X-Amz-Expires=14400&X-Amz-Date=20240727T120946Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240727/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=f633f2844a63f5b2c85cbeca2f9c08db7e771e798eb53496db2dbea2b8a2d265)
Word Embeddings for Clinical Systems
Auteur
Hathaitorn Rojnirun, Oluseye Bankole
Last Updated
il y a 5 ans
License
Creative Commons CC BY 4.0
Résumé
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=20240727T120946Z&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAWJBOALPNFPV7PVH5/20240727/us-east-1/s3/aws4_request&X-Amz-SignedHeaders=host&X-Amz-Signature=f633f2844a63f5b2c85cbeca2f9c08db7e771e798eb53496db2dbea2b8a2d265)