  Identifying patient cohorts from clinical notes in secondary electronic
health records is a fundamental task in clinical information management.
However, with the growing number of clinical notes, it becomes challenging to
analyze the data manually for phenotype detection. Automatic extraction of
clinical concepts would helps to identify the patient phenotypes correctly.
This paper proposes a novel hybrid model for automatically extracting patient
phenotypes using natural language processing and deep learning models to
determine the patient phenotypes without dictionaries and human intervention.
The model is based on a neural bidirectional sequence model (BiLSTM or BiGRU)
and a CNN layer for phenotypes identification. An extra CNN layer is run
parallel to the hybrid model to extract more features related to each
phenotype. We used pre-trained embeddings such as FastText and Word2vec
separately as the input layers to evaluate other embedding's performance.
Experimental results using MIMIC III database in internal comparison
demonstrate that the proposed model achieved significant performance
improvement over existing models. The enhanced version of our model with an
extra CNN layer obtained a relatively higher F1-score than the original hybrid
model. We also showed that BiGRU layer with FastText embedding had better
performance than BiLSTM layer to identify patient phenotypes.
