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机器学习预测急性心肌梗塞的可能性
Martin P Than1, John W Pickering1,2, Yader Sandoval3
1Emergency Department, Christchurch Hospital, New Zealand (M.P.T., J.W.P.).
Circulation
|August 17, 2019
概括
机器学习通过整合年龄,性别和激素水平来准确评估心肌梗塞风险. 这种工具,即MI3, 改善了个体患者的诊断决策.
科学领域:
- 心脏病学
- 生物医学工程
- 机器学习
背景情况:
- 目前用于心肌梗塞 (MI) 的诊断方法不考虑基于年龄,性别和采样时间的心脏度变化.
- 个人风险评估对于及时准确地诊断心脏病发作至关重要.
研究的目的:
- 开发和验证一个机器学习算法 (MI3),该算法将年龄,性别和高灵敏性心脏托波宁I度整合在一起,以改善1型心肌梗塞风险的评估.
- 提供个性化和客观的MI可能性测量.
主要方法:
- 一种渐变增强机器学习算法MI3在3013名患者中进行了训练,并在7998名疑似心脏病患者中进行了测试.
- 该算法计算风险得分 (0-100) 并估计个别诊断性能指标 (灵敏度,NPV,特异性,PPV).
- 使用校准和接收器操作特征曲线 (AUC- ROC) 的面积来评估性能,并将MI3值与现有的临床途径进行比较.
主要成果:
- 在试验组中,MI3显示出优异的校准和高AUC- ROC值为0. 963.
- 特定的MI3值确定了低风险患者 (<1. 6) 的NPV为99. 7%和高风险患者 (≥49. 7) 的PPV为71. 8%.
- 在确定心脏病发作时,MI3优于欧洲心脏病学会的0/3小时排除途径和99百分位方法.
结论:
- MI3算法提供了个性化和客观的心肌梗塞概率评估.
- 这种工具可以帮助识别低风险和高风险的患者,促进更早,更明智的临床决策.
- 机器学习的整合提高了诊断的准确性,
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