臨床的ナラティブと構造的なフェノタイプを,大規模な言語モデルと文変換器で橋渡しする
Jihao Cai1, Guozhuang Li1, Yongxin Yang2
1Department of Orthopaedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Beijing Key of Big Data Innovation and Application for Skeletal Health Medical Care, Beijing, 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Journal of genetics and genomics = Yi chuan xue bao
|February 16, 2026
まとめ
私たちは,電子医療記録から構造化されたフェノタイプを抽出するための新しい枠組みであるLEAP (LLM-Enhanced Automated Phenotyping) を開発しました. LEAPは,遺伝子研究および臨床応用のためのフェノタイプデータの正確性と信頼性を大幅に改善します.
科学分野:
- 計算生物学とは,計算生物学である.
- 医療情報工学 医療情報工学
- ゲノミクスゲノミクスとは
背景:
- 構造化されたフェノタイプは,メンデルの障害の診断と遺伝子研究において極めて重要です.
- 電子医療記録 (EHR) には,膨大な現象型データが含まれていますが,基本的には構造化されていません.
- 既存の自動化されたフェノタイプ化方法は,臨床ナラティブにおける意味学的な変動性と文脈的情報と闘っています.
研究 の 目的:
- 現在のディープラーニングモデルの限界に対処する高度な自動化フェノタイプ化フレームワークを開発する.
- 非構造化された臨床テキストから標準化されたヒューマン・フェノタイプ・オントロジー (HPO) 識別子の抽出を改善する.
- 遺伝子研究と臨床的意思決定支援のためのEHRデータの有用性を高める.
主な方法:
- 提案されたLEAPは,フェノタイプ抽出のための大型言語モデル (LLM) と,HPOマッピングのための微調整された文変換器を組み合わせた2段階のフレームワークです.
- LLMコンポーネントは,テキストの断片化なしで長い臨床ナラティブを処理します.
- 文変換器モデルは,正確で決定的なHPO識別子生成のために530万以上のインスタンスで訓練されています.
主要な成果:
- LEAPは,既存のツールと比較して,現実のEHRテストセットで精度 (19.68% -412.68%) とF1スコア (44.14% -298.77%) の相対的な改善を示しました.
- 外部ベンチマークで堅実なパフォーマンスを達成し,その汎用性を検証しました.
- このフレームワークは,有効で決定的なHPO識別子の出力を確保し,LLMの制限を克服します.
結論:
- LEAPは,非構造化されたEHRデータから自動化されたフェノタイプ化のための堅牢で正確なソリューションを提供しています.
- このフレームワークは,遺伝子優先順位付けを含む下流分析のためのフェノタイプデータの標準化と可用性を強化します.
- LEAPは,臨床情報工学と精密医療のためのAIの活用における重要な進歩を表しています.
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