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ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

2019/04/10 by Kexin Huang, Huang, Kexin, Jaan Altosaar +3 · 137 citations
Computer Science · Health Professions · #Computation and Language (cs.CL) #Electronic Health Records Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Topic Modeling #cs.CL #cs.LG

paper · pdf · doi:10.48550/arxiv.1904.05342

CHIL 2020 Workshop

openalex publication_date 2019/04/10 · openalex created_date 2019/04/25 · arxiv created 2020/11/29 · arxiv updated 2020/12/01 · openalex updated_date 2026/07/28

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.

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