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Updated: Jan 9, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

463

一种基于LSTM的时间动态神经SIR方法,用于随机传染病预测.

Sofia Zahri, Nikesh Bajaj, Jesus Requena Carrion

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    神经SIR通过将随机动态整合到易受感染-康复 (SIR) 模型中来增强传染病预测. 该框架严格评估机器学习模型在不确定的流行病情景中可靠性和稳定性.

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    In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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    科学领域:

    • 流行病学和公共卫生.
    • 计算生物学和机器学习

    背景情况:

    • 准确的传染病预测对于公共卫生干预和资源分配至关重要.
    • 传统的流行病学模型,如易受感染-康复 (SIR) 与现实世界的变化和不确定性作斗争.
    • 结合数学建模和机器学习 (ML) 的混合模型具有前景,但需要对概括性和可解释性进行评估.

    研究的目的:

    • 介绍Neural-SIR,一种基于模拟的框架,用于评估流行病预测中的ML模型概括.
    • 在不确定的场景下评估ML模型的可靠性,稳定性和流行病学解释性.
    • 纳入随机动态,以更好地反映现实世界的疾病变化.

    主要方法:

    • 开发了神经SIR,一个使用SIR微分方程 (DE) 的时间动态模拟框架.
    • 使用两种不确定性建模方法,生成了捕捉区域间异质性和人口内部变异性的综合数据.
    • 训练并评估了使用平均绝对误差 (MAE),根平均平方误差 (RMSE) 和R平方的长短期记忆 (LSTM) 模型.

    主要成果:

    • 神经SIR框架成功地整合了随机动态,模拟了现实世界的不确定性.
    • 各种LSTM架构的性能评估表明它们在新的,不确定的条件下具有概括能力.
    • 调查结果强调了随机动态对于可靠的流行病预测模型评估的重要性.

    结论:

    • 神经SIR为在不确定性条件下评估流行病预测模型提供了严格和可解释的框架.
    • 该框架能够纳入随机动态的能力提高了预测方法的可靠性和稳定性.
    • 这有助于公共卫生专业人员和临床医生在管理疾病爆发时做出更好的决策.

    相关实验视频

    Last Updated: Jan 9, 2026

    A Data-Driven Approach to Quantifying Immune States in Sepsis
    07:42

    A Data-Driven Approach to Quantifying Immune States in Sepsis

    Published on: February 7, 2025

    463