機械学習による妊娠前出血症の予測: 体系的な文献レビュー
1Department of Management Information Systems, İzmir Bakırçay University, İzmir, Türkiye.
Health systems (Basingstoke, England)
|August 21, 2025
まとめ
機械学習モデルは年齢や血圧などの 共通の要因を使って 妊娠前出血症を予測できます この妊娠合併症の早期発見には より多様なデータが必要です.
科学分野:
- 産婦人科
- 医療情報学
- コンピュータ生物学
背景:
- 妊娠前出血症は原因と危険因子の不明な重篤な妊娠合併症です.
- 妊娠前出血症の早期予測は 適切な介入と 妊婦の改善に不可欠です
- マシンラーニング (ML) は,妊娠前出血症の予測モデルを開発するための強力なツールです.
研究 の 目的:
- 妊娠前出血症の予測のための最新の機械学習の研究を体系的にレビューし,分析する.
- MLベースの妊娠前出血症の研究における主要な特徴,アルゴリズム,および地理的傾向を特定する.
- 妊娠前出血症の検出におけるMLモデルの強化のための限界と将来の方向性を強調する.
主な方法:
- 2013年1月1日から2023年12月31日までの間に発表された研究の体系的な文献レビュー.
- Google ScholarとPubMedで実施された検索で,183件の研究が特定され,35件が含める基準に基づいて選択されました.
- 共通の予測機能,MLアルゴリズム,研究場所,データセットの特徴の分析.
主要な成果:
- 一般的に使用される予測特徴には,母親の年齢,妊娠歴,体量指数,糖尿病,高血圧,および血圧が含まれます.
- 薬剤,遺伝子データ,臨床イメージングはあまり使われなかった.
- 人気のあるMLアルゴリズムは,ランダムフォレスト,サポートベクトルマシン,ロジスティック回帰,意思決定ツリー,ナイヴベイズです.
- 研究は中国と米国に集中しており,最近出版物が急増していますが,しばしば小さな単一センターデータセットに依存しています.
結論:
- 機械学習は,容易に入手可能な臨床データを活用して,妊娠前出血症の予測に大きな可能性を秘めている.
- 現在の研究環境では,モデルの一般化性を改善するために,より多様なデータセットとマルチセンター研究が必要です.
- 妊娠前出血症の早期発見と効果的な管理のためのMLモデルを改良するために,さらなる研究が不可欠です.
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