arXiv:1904.05342
공식 코드 · Papers with Code
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
원문 초록
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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Zero Shot Health Trajectory Prediction Using Transformer
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Large Language Models for Integrating Social Determinant of Health Data: A Case Study on Heart Failure 30-Day Readmission Prediction
CPLLM: Clinical Prediction with Large Language Models
Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare
Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction
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