Clinicalbert: Modeling clinical notes and predicting hospital readmission
K Huang, J Altosaar, R Ranganath - arXiv preprint arXiv:1904.05342, 2019 - arxiv.org
K Huang, J Altosaar, R Ranganath
arXiv preprint arXiv:1904.05342, 2019•arxiv.orgClinical notes contain information about patients that goes beyond structured data like lab
values and medications. However, clinical notes have been underused relative to structured
data, because notes are high-dimensional and sparse. This work develops and evaluates
representations of clinical notes using bidirectional transformers (ClinicalBERT).
ClinicalBERT uncovers high-quality relationships between medical concepts as judged by
humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction …
values and medications. However, clinical notes have been underused relative to structured
data, because notes are high-dimensional and sparse. This work develops and evaluates
representations of clinical notes using bidirectional transformers (ClinicalBERT).
ClinicalBERT uncovers high-quality relationships between medical concepts as judged by
humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction …
Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work develops and evaluates representations of clinical notes using bidirectional transformers (ClinicalBERT). ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit. Code and model parameters are available.
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