The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis.
The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis.
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DOI：
10.3389/fphys.2021.716434
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发表时间：
2021
影响因子：
4
通讯作者：
Cockrell RC
中科院分区：
文献类型：
--
作者：
Larie D;An G;Cockrell RC
简介：同时通过生物标志物或基因表达谱以越来越精细的分辨率来表征疾病状态。机器学习 (ML) 越来越多地用于分析生物系统的行为，并可能根据此类特征进行分类或预测。由于 ML 应用的数据极其密集，考虑到生物医学数据集相对稀疏，人工神经网络 (ANN) 的 ML 训练通常需要使用合成训练数据。基于代理的模型（ABM）结合了已知的生物机制及其相关的随机特性，是生成合成数据的潜在手段。在此，我们展示了一个用于训练人工神经网络 (ANN) 的 ML 示例，作为替代系统，用于预测 ABM 的时间演化，重点关注脓毒症的临床状况。方法：根据时间细胞因子和表型动力学，临床脓毒症的疾病轨迹可以解释为随机动力系统。基于先天免疫反应因子的模型（IIRABM）是一种成熟的模型，利用已知的细胞和分子规则来模拟与临床脓毒症相对应的疾病轨迹。我们利用两种不同的神经网络架构，即长短期记忆和多层感知器，将十一个 IIRABM 模拟血清细胞因子浓度的五次测量的时间序列作为输入，并返回未来的细胞因子轨迹以及代表患者健康状况的聚合指标。结果：由于模拟中的随机性，人工神经网络预测的模型轨迹具有预期的误差量，并且认识到从特定细胞因子谱到健康状态的映射并不是唯一的。多层感知器神经网络通过更准确的预测轨迹锥生成预测。讨论：这项工作作为使用 ANN 预测以 ABM 为代表的脓毒症疾病进展的概念验证。研究结果表明，具有内在随机性的多细胞系统可以用人工神经网络进行近似，但预测系统的特定轨迹需要连续更新系统状态以提供滚动预测范围。
Introduction: Disease states are being characterized at finer and finer levels of resolution via biomarker or gene expression profiles, while at the same time. Machine learning (ML) is increasingly used to analyze and potentially classify or predict the behavior of biological systems based on such characterization. As ML applications are extremely data-intensive, given the relative sparsity of biomedical data sets ML training of artificial neural networks (ANNs) often require the use of synthetic training data. Agent-based models (ABMs) that incorporate known biological mechanisms and their associated stochastic properties are a potential means of generating synthetic data. Herein we present an example of ML used to train an artificial neural network (ANN) as a surrogate system used to predict the time evolution of an ABM focusing on the clinical condition of sepsis. Methods: The disease trajectories for clinical sepsis, in terms of temporal cytokine and phenotypic dynamics, can be interpreted as a random dynamical system. The Innate Immune Response Agent-based Model (IIRABM) is a well-established model that utilizes known cellular and molecular rules to simulate disease trajectories corresponding to clinical sepsis. We have utilized two distinct neural network architectures, Long Short-Term Memory and Multi-Layer Perceptron, to take a time sequence of five measurements of eleven IIRABM simulated serum cytokine concentrations as input and to return both the future cytokine trajectories as well as an aggregate metric representing the patient’s state of health. Results: The ANNs predicted model trajectories with the expected amount of error, due to stochasticity in the simulation, and recognizing that the mapping from a specific cytokine profile to a state-of-health is not unique. The Multi-Layer Perceptron neural network, generated predictions with a more accurate forecasted trajectory cone. Discussion: This work serves as a proof-of-concept for the use of ANNs to predict disease progression in sepsis as represented by an ABM. The findings demonstrate that multicellular systems with intrinsic stochasticity can be approximated with an ANN, but that forecasting a specific trajectory of the system requires sequential updating of the system state to provide a rolling forecast horizon.
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DOI：
10.1038/nri3552
发表时间：
2013-12
期刊：
Nature reviews. Immunology
影响因子：
--
作者：
通讯作者：
--
影响因子：
120.7
作者：
Boomer, Jonathan S.;To, Kathleen;Chang, Kathy C.;Takasu, Osamu;Osborne, Dale F.;Walton, Andrew H.;Bricker, Traci L.;Jarman, Stephen D., II;Kreisel, Daniel;Krupnick, Alexander S.;Srivastava, Anil;Swanson, Paul E.;Green, Jonathan M.;Hotchkiss, Richard S.
通讯作者：
Hotchkiss, Richard S.
影响因子：
1.3
作者：
Bhattacharya, R;Majumdar, M
通讯作者：
Majumdar, M
影响因子：
--
作者：
Lagaris, IE;Likas, A;Fotiadis, DI
通讯作者：
Fotiadis, DI
影响因子：
2.7
作者：
Bergquist, Maria;Hastbacka, Johanna;Lipcsey, Miklos
通讯作者：
Lipcsey, Miklos