Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Kexin Huang, Jaan Altosaar, Rajesh Ranganath

Publisher name:  arXiv:1904.05342

DOI:  10.48550/arXiv.1904.05342

Volume Information:  

Paper Link:   https://arxiv.org/abs/1904.05342