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English(EN) Physics-Informed Neural Networks for Chemotherapy Pharmacokinetics: Benchmarking the Clinical Estimator and Exposing Parameter Identifiability

PINNs 通过揭示参数可辨识性来推进化疗建模

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了物理信息神经网络（PINNs）来模拟化疗药代动力学，在复杂场景下表现优于传统方法。PINNs 可以准确预测组织中的药物浓度，这对于确定治疗效果和毒性至关重要，甚至可以识别何时模型无法从可用数据中辨识。这种方法提供了一种统一的方法来分析具有部分观测的生物系统，将已知的物理动力学与测量数据相结合。 AI

影响 PINNs 为分析复杂的生物系统提供了一种更稳健的方法，通过揭示模型局限性，有可能改善药物开发和个性化医疗。

排序理由 该集群包含一篇详细介绍 PINNs 在特定科学问题上的新应用的论文。

在 arXiv stat.ML 阅读 →

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PINNs 通过揭示参数可辨识性来推进化疗建模

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Riya Bisht, Dhruv Agarwal ·

    用于化疗药代动力学的物理信息神经网络：临床估算器基准测试与参数可辨识性揭示

    arXiv:2606.12658v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) are an attractive tool for partial-observation problems in biology, where the governing dynamics are known but some compartments cannot be measured. Chemotherapy pharmacokinetics (PK) is a …

  2. arXiv stat.ML TIER_1 English(EN) · Dhruv Agarwal ·

    用于化疗药代动力学的物理信息神经网络：临床估算器基准测试与参数可辨识性揭示

    Physics-Informed Neural Networks (PINNs) are an attractive tool for partial-observation problems in biology, where the governing dynamics are known but some compartments cannot be measured. Chemotherapy pharmacokinetics (PK) is a clean instance: drug concentration in plasma is ro…

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