ドイツの臨床テキストにおけるICD-10コードの検索拡張生成 - テクニカルケースレポート
Mario Krumscheid1,2, Johannes Blömer3, Matthias Becker1,2
1Department of Computer Science, Kempten University of Applied Sciences, Kempten, Germany.
Studies in health technology and informatics
|September 3, 2025
まとめ
この研究は,ドイツの臨床文書の国際疾病分類 (ICD) の半自動化パイプラインを導入し,医療文書の効率と正確性を向上させます.
科学分野:
- 医療情報学
- 自然言語処理
- 臨床文書
背景:
- マニュアル国際疾病分類 (ICD) のドイツ語の臨床文献のコーディングは非効率で誤りやすい.
- 既存の自動化された方法は 資源が少ない言語と 希少なコードで苦労しています
研究 の 目的:
- ドイツの構造化されていない医療文書の効率的なICDコーディングのための半自動パイプラインを開発する.
- ドイツの臨床テキスト分析における現在の微調整された言語モデルの限界に対処する.
主な方法:
- 復元拡張生成 (RAG) フレームワークを導入し,エンティティ認識,意味復元,および略語の解消を行いました.
- Sentence-BERTの埋め込み,FAISSのインデックス化,ICDコードの割り当てのためのMistral-Small-Instructの生成モデルを使用した.
- 精密なコード選択のための 統合された意味の類似性と 生成的精細化.
主要な成果:
- 開発されたパイプラインは,半自動ICDコーディングのための複数のNLP技術を統合しています.
- 誤差を減らすための鍵となる領域として,意味の検索と診断の正規化を特定しました.
- 低リソースの臨床テキストコーディングの課題に対処するための新しいアプローチを示しました.
結論:
- RAGパイプラインは,ドイツ語の半自動ICDコーディングのための有望なアプローチを提供します.
- 将来の作業は,より高い精度のために,領域特有の埋め込みと文脈意識の誘導を強化する必要があります.
- このシステムは,実際の環境での臨床コードの正確性と使用性を向上させることを目的としています.
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