前立腺がんの診察における医師のコミュニケーションの質を評価するための自然言語処理システムの開発と検証
Renning Zheng1,2, Nadine A Friedrich1, Michael Luu3
1Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Prostate cancer and prostatic diseases
|August 22, 2025
まとめ
自然言語処理 (NLP) モデルは前立腺がんの診察において 医師のコミュニケーションの質を正確に評価できます これらのモデルにより 患者は治療の選択肢に関する 共通の意思決定のための 重要な情報を得ることができます
科学分野:
- 泌尿器科
- 医療情報工学
- 健康に関する情報
背景:
- AUAのガイドラインは,前立腺がんのケアにおける共同意思決定 (SDM) を強調し,腫瘍のリスク,寿命,予後,ベースライン機能,および治療の副作用の議論を必要とする.
- 患者の情報保持と医者のリスクコミュニケーションの不一致は SDMの効果を阻害しています.
- 自然言語処理 (NLP) は,情報提供とコミュニケーション品質の評価を改善するための潜在的な解決策を提供します.
研究 の 目的:
- 前立腺がんの診察における重要な情報を特定するためのNLPモデルを開発し,検証する.
- 特定の内容に関する医師のコミュニケーションの質を監査するNLPモデルの能力を評価する.
主な方法:
- SDMの重要な概念に関連する文を特定するために,50件のコンサルティングトランスクリプトでNLPモデルを訓練し,検証しました.
- トップで特定された文を使って20回の別々のコンサルテーションでコミュニケーションの質を評価するモデルパフォーマンスをテストしました.
主要な成果:
- ランダムフォレストNLPモデルは,トピックに一致する文の識別において高い精度 (ほとんどの概念のAUC> 0.80) を示した.
- NLPモデルの上位で特定された文は,異なる概念の80%から100%の精度で,全体のコンサルティングにおけるコミュニケーションの質を正確に格付けしました.
結論:
- NLPモデルは重要な情報を効果的に把握し,前立腺がんの診察で医師のコミュニケーションの質を評価します.
- これらのモデルは,リスクコミュニケーションを評価し,患者の情報に基づいた意思決定を支援するためのスケーラブルな基盤を提供します.
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