ビッグデータからAI主導の意思決定へ 阻害性睡眠時無呼吸症:DDPPフレームワークを統合したナラティブレビュー
Mengying Wu1,2, Kexin Wang2, Huai Huang2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
Nature and science of sleep
|August 27, 2025
まとめ
阻害性睡眠無呼吸 (OSA) 研究における人工知能 (AI) は,睡眠信号に焦点を当てながら,他のデータを無視している. パーソナライズド・トリートメント・アナリティクス (処方) は希少で,臨床的意思決定を妨げている.
科学分野:
- 睡眠医学
- 人工知能
- データサイエンス
背景:
- 阻害性睡眠無呼吸 (OSA) は 診断が不十分で 治療が不十分であることが多い疾患です
- 人工知能 (AI) の進歩は,OSA管理のための潜在的な解決策を提供します.
- OSAのマルチモダルのデータリソースは急速に拡大しています.
研究 の 目的:
- 阻害性睡眠無呼吸 (OSA) のデータ資源におけるAI技術の活用を包括的に検討する.
- 異なるデータ型と分析段階 (記述的,診断的,予測的,規定的) にわたるAIの適用を分析する.
- OSAの現在のAIアプリケーションのギャップを特定し,将来の研究のための勧告を提供します.
主な方法:
- 2020年4月から2025年4月まで,主要な科学データベース (PubMed,Web of Science,Scopus,IEEE Xplore) で体系的な文献検索が行われました.
- AI関連のキーワードと "睡眠時無呼吸症候群"を組み合わせた検索条件です
- DDPP (記述的,診断的,予測的,規定的) アナリティクスモデルは,AIアプリケーションを分類するために使用されました.
主要な成果:
- 575件の研究のうち ほとんどのAI研究は 睡眠モニタリングの信号に焦点を当てていました
- 患者が報告した結果,電子医療記録,環境データは 未だに十分に利用されていない.
- 現在の臨床アプリケーションは,主に説明的および診断的AIに焦点を当て,パーソナライズされた治療のための制限された処方分析があります.
結論:
- 入手可能なOSAデータと現在のAIアプリケーションの間には大きなギャップがあります.
- 将来の研究は,様々なOSAデータ次元を活用し,適切なAIテクノロジーを選択する必要があります.
- AIの活用により,OSAの臨床的意思決定と個別化された治療が改善されます.
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