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使用深度学习和残余卷积网络进行有效的败血症检测.

Ahmed S Almasoud1, Ghada Moh Samir Elhessewi2, Munya A Arasi3

  • 1Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia.

PeerJ. Computer science
|September 24, 2025
PubMed
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此摘要是机器生成的。

一个新的深度学习模型与非洲优化算法 (AVOA) 结合起来,显著提高了败血症检测的准确性. 这种先进的系统提供了更可靠和及时的诊断,对于患者的生存和治疗有效性至关重要.

科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 计算生物学 计算生物学

背景情况:

  • 败血症是一种危及生命的疾病,需要及时诊断和治疗.
  • 当前的临床实践在及时检测败血症方面面临挑战.
  • 技术进步对于改善败血症识别至关重要.

研究的目的:

  • 开发一种新的深度学习模型,用于准确检测败血症.
  • 使用非洲优化算法 (AVOA) 提高模型性能.
  • 改善在败血症病例中的及时干预和患者结果.

主要方法:

  • 使用一个增强的卷积式学习框架 (ECLF) 与状卷积.
  • 整合了一个空间通道注意网络 (SCAN) 以集中特征学习.
  • 采用分层扩展卷积块 (HDCB) 和残余路径卷积链 (RPCC) 来进行特征提取和传播.
  • 使用非洲优化算法 (AVOA) 优化模型.

主要成果:

  • 实现了高精度 (99.4%),精度 (98%),回忆 (99.2%) 和F1得分 (99.0%).
  • 证明了0.998.99的曲线下的优越面积 (AUC).
关键词:
非洲的优化算法深度学习是一种深度学习.扩展的卷积卷积.增强的卷积学习.败血症检测检测的检测方法空间频道的注意力.

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  • 超过了传统的临床评分和传统的机器学习方法.

    • 结论:

    • 建议使用AVOA的深度学习模型在败血症检测方面表现出卓越的准确性和可靠性.
    • 这种方法有助于及时干预,可能改善患者的治疗结果.
    • 该模型显示复杂的医疗数据集的稳定性和可转移性.