This study leverages BERT and Bi-LSTM (Bidirectional LSTM) for precise phenotype detection in clinical notes, using pre-existing high-recall NLP annotations, vetted by clinicians. The dataset, a subset of the MTSamples corpus, includes 498 documents and their annotations.
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This study leverages BERT and Bi-LSTM (Bidirectional LSTM) for precise phenotype detection in clinical notes, using pre-existing high-recall NLP annotations, vetted by clinicians. The dataset, a subset of the MTSamples corpus, includes 498 documents and their annotations.
rsr-droid/ner-clinical-notes
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This study leverages BERT and Bi-LSTM (Bidirectional LSTM) for precise phenotype detection in clinical notes, using pre-existing high-recall NLP annotations, vetted by clinicians. The dataset, a subset of the MTSamples corpus, includes 498 documents and their annotations.
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