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LLM主導のガイドライン遵守による呼吸器支援の予測モデリングの強化

Xiaolei Lu1, Michael Miller1, Alex K Pearce1

  • 1University of California, San Diego.

Research square
|August 20, 2025
PubMed
まとめ

大規模言語モデル (LLM) と深層対事実モデルを統合することで,集中治療室 (ICU) の患者の呼吸支援の推奨が改善され,侵入的機械呼吸器 (IMV) 率と死亡率が低下しました.

科学分野:

  • クリティカル ケア 医療
  • 医療における人工知能
  • 呼吸器療法

背景:

  • 高流量鼻カヌラ (HFNC) と非侵襲的呼吸器 (NIV) の間の最適の選択は,侵襲的機械呼吸器 (IMV) のリスクのある集中治療室 (ICU) の患者にとって不明である.
  • 以前の深層反事実モデル (RepFlow-CFR) は,解釈可能性と臨床ガイドラインの整合性が欠けていました.
  • この研究は,臨床ガイドラインに基づくLLMを統合することで,これらの課題に取り組んでいます.

研究 の 目的:

  • 臨床ガイドラインに基づくLLMを開発し統合し,NIVとHFNCの深い対事実モデル推奨を強化する.
  • 高リスクのICU患者における呼吸器支援に関する決定の解釈性と臨床ガイドラインの遵守を改善する.
  • LLMで改善された勧告が患者と臨床実務に与える影響を評価する.

主な方法:

  • ガイドラインの遵守と説明可能な勧告のための大規模な言語モデル (LLM,Claude 3.5 Sonnet) を組み込むことでRepFlow-CFRモデルを改良しました.
  • HIPAAに適合するAWS環境でLLMを構成し,構造化された患者データ,臨床ノート,および誘導のためのガイドライン基準を使用しました.
  • LLMで改善された勧告と実際の治療決定を比較し,侵襲的機械呼吸器 (IMV) と死亡率/ホスピス率を評価した. 臨床的有効性と安全性を確認した.

主要な成果:

キーワード:
原因推論ガイドラインの遵守高流量鼻カヌーラ個別化された治療効果大型言語モデル非侵襲的な換気

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  • LLMで強化された勧告に一致する治療は,IMV率を著しく低下させ (24. 47%対52. 94%) 死亡率またはホスピス退院率を低下させた (OR=0. 670,p=0. 046).
  • 20件の病状レビューでは,LLMの勧告の95%が臨床ガイドラインと一致し,医師は最終的な勧告の65%に同意した.
  • 11件中20件でエラーが確認され,そのほとんどは低リスクか中程度のリスクとみられ,2件のみが重害を及ぼす可能性があると評価された.

結論:

  • LLMを統合することで,呼吸器支援の意思決定のための対事実的モデルの解釈性と臨床的調整が向上します.
  • このハイブリッド・フレームワークは現実世界の実践と一致し 患者の改善の可能性を示しています
  • 将来の研究は,潜在的な臨床試験を通じて,禁忌の検出の精錬と検証の拡大に焦点を当てます.