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関連する概念動画

Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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プライマリケアにおける銃器暴力の暴露を特定する 臨床ノート: 国語処理テキスト分類器の開発のためのプロトコル

Natalie Carwright1, Frances M Biel2, Megan Hoopes2

  • 1Department of Mathematics, Norwich University, Northfield, VT, United States.

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まとめ

この研究は,電子医療記録における銃器暴力の暴露を特定するための新しい自然言語処理 (NLP) テキスト分類器を開発しました. このツールは 銃による暴力が 健康に与える影響を よりよく理解することで 患者のケアを改善することを目的としています

キーワード:
銃による怪我銃による暴力自然言語処理,電子医療記録テキスト分類器

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科学分野:

  • 公衆衛生
  • 医療情報学
  • コンピュータ言語学

背景:

  • 構造化された電子医療記録 (EHR) のデータは,二次的な経験を含む銃器暴力の全範囲を十分に捉えていません.
  • 暴力を目の当たりにしたり 愛する人を亡くしたりすると 短期的にも長期的にも 深刻な健康問題が生じます
  • EHR内の臨床ノートには,これらの暴露を特定するために利用できる豊富な非構造データが含まれています.

研究 の 目的:

  • 原発的および二次的な銃器暴力の暴露を識別するための自然言語処理 (NLP) テキスト分類器を開発する.
  • 5歳以上の患者について, ambulatory primary careおよび行動健康に関する臨床ノートから得られた暴露データを分析する.
  • 臨床環境での銃による暴力の確認を強化する.

主な方法:

  • 2012年から2022年の間,EHRネットワークであるOCHINの非構造化された臨床ノートを使用しています.
  • レキシコン識別とマニュアルテキストレビューによるラベル付きデータセットの開発
  • テキスト分類のための機械学習,ニューラルネットワーク,および大規模な言語モデルを構築し,訓練し,評価します.
  • 方法論的厳格性を確保し,潜在的なバイアスを対処するために,利害関係者諮問委員会を巻き込む.

主要な成果:

  • この研究は現在,NLPテキスト分類器の評価段階にあり,2025年8月までに最終的なモデル選択が予想されています.
  • NLPモデルの開発とパフォーマンスの結果は2026年に発表される予定です.
  • 銃による暴力を特定するためのNLPモデルの開発と評価.

結論:

  • この研究は,臨床ノート内の銃器暴力の暴露を特定するための新しいNLPテキスト分類器を導入します.
  • 開発されたNLPモデルは,暴露された患者の識別を高める可能性がある.
  • この研究は 銃による暴力の長期的な健康への影響を理解し 患者のケア戦略を改善するための基礎を築いています