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使用人工智能对死亡原因进行实时分类:灵敏度分析分析.

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  • 1Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal.

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概括
此摘要是机器生成的。

AUTOCOD深度神经网络可靠地从医生死亡证明中确定死亡原因,即使在死亡率高的时期. 它的性能保持一致,确保准确的实时死亡率监测.

关键词:
在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.深度学习是一种深度学习.深度神经网络是一个神经网络.评价 评价 评价 评价机器学习是机器学习.死亡率 死亡率死亡率统计 死亡率统计这是死亡的根本原因.

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科学领域:

  • 公共卫生监督 公共卫生监督
  • 人工智能在医学中的应用
  • 死亡率数据分析 数据分析

背景情况:

  • 葡萄牙在2021年经历了超过16000例过度死亡,突出了对高效死亡率数据分析的需求.
  • 卫生总局开发了AUTOCOD,这是一个深度神经网络,用于从死亡证明中自动确定死亡原因.
  • 在死亡率过高的时期,AUTOCOD的持续性表现是公共卫生的一个关键因素,需要评估.

研究的目的:

  • 与手动编码相比,评估AUTOCOD在分类潜在死亡原因方面的灵敏度和性能指标.
  • 使用AUTOCOD. 在过度死亡期间识别特定的死亡原因.
  • 为了评估AUTOCOD在不同死亡率条件下的可靠性.

主要方法:

  • 从2016年至2019年分析了330,098份死亡证明 (DC),将AUTOCOD分类与手动编码 (黄金标准) 进行了比较.
  • 使用混矩阵计算性能指标,包括灵敏度,特异性和正预测值 (PPV).
  • 在正常,过剩,严重和极端过剩死亡率期间比较AUTOCOD性能,通过Z-scores定义过剩死亡率.

主要成果:

  • 对于10个ICD-10章节,AUTOCOD显示出高灵敏度 (≥0.75),对于诸如新生体和循环/呼吸系统疾病等流行章节超过0.90.
  • 性能指标 (灵敏度,特异性>0.96,PPV>0.75) 在有或没有过度死亡率的时期保持一致.
  • 在ICD-10区块水平上,AUTOCOD保持了高性能,在过度死亡时期没有观察到显著差异.

结论:

  • 在高压医疗服务期间,AUTOCOD的性能不受潜在的文本质量下降的影响.
  • 深度神经网络可以可靠地用于实时,因特定原因的死亡率监测,即使是在极端过度死亡事件中.
  • AUTOCOD为公共卫生决策和监测提供可靠的数据.