依存症治療のための大規模な言語モデルによる臨床ノートにおける汚名言語の検出
medRxiv : the preprint server for health sciences
|August 20, 2025
まとめ
大規模言語モデル (LLM) は,薬物使用障害 (SUD) の患者に対する集中治療室 (ICU) のメモで,汚名付けの言語を効果的に識別します. 97. 2%の精度を達成し,臨床文書のバイアスを減少させた.
科学分野:
- 自然言語処理
- 臨床情報学
- 医学 の 倫理
背景:
- 臨床ノートに刻印された言語は 患者のケアに悪影響を及ぼし 偏見を永続させることができます
- 薬物使用障害 (SUD) を患っている患者は,医療環境で特に汚名を受ける恐れがあります.
- 集中治療室 (ICU) の進捗メモは 汚名付けの潜在的な源です
研究 の 目的:
- ICUの進捗ノート内の汚名付け言語を検出する大型言語モデル (LLM) のパフォーマンスを評価する.
- ゼロショット,イン・コンテキスト・ラーニング,リトリーバルの拡張生成 (RAG),監督された微調整 (SFT) を含む異なるLLMアプローチを比較する.
- 汚名を検知する理由を説明し,新しい汚名用語を特定するLLMの能力を評価する
主な方法:
- MIMIC-IIIからの77,104のICUノートのデータセットが作成され,汚名付けと汚名付けのない出会いをバランスとしました.
- 4つのLLMアプローチ (ゼロショット,コンテキスト学習,RAG,SFT) とキーワード検索ベースラインがテストされました.
- モデルの精度とマクロF1スコアは,列車/開発/テスト分割と外部検証セットを使用して評価されました.
主要な成果:
- 監督された微調整 (SFT) は最高精度 (97.2%) を達成し,その次にコンテキスト学習が続きました.
- LLMは予測に一貫した推論を提供し,偽陽性の見直しを助けました.
- SFTとイン・コンテキスト・ラーニングの両方で,マニュアル・アノテーション中に見逃した汚名を特定しました.
結論:
- LLMは,特にSFTと文脈学習は,ICUのノートに刻印された言語を特定するのに非常に有効です.
- これらのモデルは,手作業によるレビューの効率的な代替手段であり,時間と労力を削減します.
- LLMは,臨床文書の汚名を軽減し,SUD患者のケアを改善するのに役立ちます.
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