Hybrid neural-symbolic pipeline extracts (action, date) pairs from clinical notes at 0.99 Pair F1 by using BioBERT tagging plus deterministic time normalization, outperforming LLMs on a synthetic benchmark with OOV actions.
Clinical information extraction applications: A literature review
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LLM pipeline with novel attribution algorithm extracts ROS entities, negation status, and body systems from 24 clinical notes at up to 0.952 F1 using open-source models.
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Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline
Hybrid neural-symbolic pipeline extracts (action, date) pairs from clinical notes at 0.99 Pair F1 by using BioBERT tagging plus deterministic time normalization, outperforming LLMs on a synthetic benchmark with OOV actions.
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A Large Language Model Based Pipeline for Review of Systems Entity Recognition from Clinical Notes
LLM pipeline with novel attribution algorithm extracts ROS entities, negation status, and body systems from 24 clinical notes at up to 0.952 F1 using open-source models.