Word Embeddings for Clinical Systems
Autor:
Hathaitorn Rojnirun, Oluseye Bankole
Letzte Aktualisierung:
vor 5 Jahren
Lizenz:
Creative Commons CC BY 4.0
Abstrakt:
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.
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