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Updated: Sep 9, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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自然言語処理と出院時の出血を検出するためのICD-10のコーディング:比較的横断的研究

Frederic Gaspar1,2, Mehdi Zayene3, Claire Coumau1,2,4

  • 1Center for Research and Innovation in Clinical Pharmaceutical Sciences, Rue du Bugnon 19, Lausanne, 1011, Switzerland, 41 763306834.

JMIR medical informatics
|August 29, 2025
PubMed
まとめ

自然言語処理 (NLP) モデルは,高齢者の出血による薬剤有害事象 (ADEs) の検出において,ICD-10のコードを大幅に上回ります. NLPは出血の重度と病歴を正確に特定し,患者の安全監視を改善します.

キーワード:
AI についてML についてNLPについて薬剤による有害事象人工知能出血する横断型意思決定支援システム意思決定ディープラーニング免許の要約高齢者電子医療記録医療について血流国際疾病分類ロジスティック回帰機械学習医療記録自然言語処理高齢者

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