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医療における機械学習

Rahul C Deo1

  • 1From Cardiovascular Research Institute, Department of Medicine and Institute for Human Genetics, University of California, San Francisco, and California Institute for Quantitative Biosciences, San Francisco. rahul.deo@ucsf.edu.

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|November 18, 2015
PubMed
まとめ
この要約は機械生成です。

機械学習は医療において有望ですが 臨床効果は限られています このレビューでは,医療実践に機械学習を統合する際の潜在的な応用と障害を調査します.

キーワード:
人工知能パソコン予後についてリスク要因統計について

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科学分野:

  • コンピュータ科学
  • 医療情報工学
  • データサイエンス

背景:

  • コンピューティング能力,データ可用性,アルゴリズムの進歩により,機械学習 (ML) は様々な分野で卓越しています.
  • 医療分析への関心が高まっている.

研究 の 目的:

  • 機械学習の医療応用の可能性を 検討する
  • 文献の例を使って基本的なMLの概念を紹介する.
  • MLの臨床採用を妨げている障害を特定し,解決策を提案する.

主な方法:

  • 医学における機械学習の応用に関する文献レビュー
  • 医療におけるMLに関する既存の研究の分析
  • 課題を特定し,それを克服するための戦略を策定する.

主要な成果:

  • 入手可能なデータとアルゴリズムにもかかわらず,MLは他の業界と比較して臨床ケアに限られた影響を及ぼしています.
  • 何千もの論文が 医学的なデータに ML を適用していますが 有意義な臨床的改善に 繋がる論文はほとんどありません

結論:

  • 医学における機械学習の広範な臨床採用を妨げている大きな障害があります.
  • これらの障壁を克服することは 医療慣行を変革するMLの可能性を 実現するために不可欠です