機械学習が急性心筋梗塞の確率を予測する

Martin P Than¹, John W Pickering^1,2, Yader Sandoval³

¹Emergency Department, Christchurch Hospital, New Zealand (M.P.T., J.W.P.).

Circulation

|August 17, 2019

まとめ

機械学習は年齢,性別,トロポニンレベルを統合することで心筋梗塞のリスクを正確に評価しますこのMI3は個々の患者の診断決定を改善します

科学分野:

心臓病科
生物医学工学
機械学習

背景:

心筋梗塞 (MI) の現在の診断方法では,年齢,性別,採取時間による心臓トロポニン濃度の変動は考慮されていません.
個別的なリスク評価は MIの早期かつ正確な診断に不可欠です.

研究の目的:

1型心筋梗塞のリスクの評価を改善するために,年齢,性別,高感度心筋トロポニンI濃度を統合した機械学習アルゴリズム (MI3) の開発と検証.
MIの確率を個別化して客観的に測定する

主な方法:

グラデーション強化機械学習アルゴリズムMI3は,3013人の患者で訓練され,MIの疑いのある7998人の患者でテストされました.
アルゴリズムはリスクスコア (0-100) を計算し,個々の診断性能指標 (感度,NPV,特異性,PPV) を推定する.
MI3の値と既存の臨床経路を比較して,キャリブレーションと受容器の動作特性曲線 (AUC- ROC) 以下の領域を用いてパフォーマンスを評価した.

主要な成果:

MI3は優れた校正と高AUC-ROCの0. 963をテストセットで示した.
特定MI3の値では,低リスク患者 (<1. 6),NPV99. 7%,高リスク患者 (≥49. 7),PPV71. 8%が特定されました.
MI3は,欧州心臓病学協会の 0/3時間排除経路と,MIを特定する99パーセントの方法よりも優れている.

結論:

MI3アルゴリズムは,心筋梗塞の確率を個別化して客観的に評価します.
このツールは,低リスクと高リスクの患者を特定し,早期により情報に基づいた臨床決定を容易にするのに役立ちます.
機械学習の統合は診断の精度を伝統的な方法よりも高めます

キーワード:

急性冠動脈症候群機械学習心筋梗塞トロポニン

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