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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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一个高效的患者反应预测系统,使用多尺度扩展组合网络框架与优化策略.

Nalini Manogaran1, Nirupama Panabakam2, Durai Selvaraj3

  • 1Department of CSE, S.A. Engineering College (Autonomous), Chennai, 600077, Tamil Nadu, India.

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概括

本研究引入了一种深度学习 (DL) 模型,用于预测患者对放射治疗和化疗的反应,改进治疗计划并减少副作用. 多尺度扩展整体网络 (MDEN) 提高了预测准确性,并最大限度地减少了错误.

关键词:
短期长期记忆 短期长期记忆多尺度扩展集体网络多尺度扩展集体网络一维卷积神经网络是一维卷积神经网络.预测患者的反应.经常性的神经网络.基于重复勘探和开采的coati优化算法

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科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 放射治疗和化疗是癌症治疗的重要方法,但可能导致严重的副作用,如心血管疾病和肺纤维化.
  • 准确预测患者反应和毒性对于个性化治疗规划和改善结果至关重要.
  • 现有的方法,如卷积神经网络 (CNN),显示出希望,但需要进一步增强精确的预测.

研究的目的:

  • 开发基于深度学习 (DL) 的放射治疗和化疗患者反应预测系统.
  • 准确预测患者的反应和预后,使得早期的治疗决策.
  • 通过精确的预测,尽量减少与治疗相关的毒性,并提高整体患者护理.

主要方法:

  • 开发了一个深度学习 (DL) 模型,集成长期短期记忆 (LSTM),循环神经网络 (RNN) 和一维卷积神经网络 (1DCNN).
  • 基于重复探索和利用的Coati优化算法 (REE-COA) 用于从手动收集的患者数据中进行最佳特征选择.
  • 为了预测,采用了多尺度扩展组合网络 (MDEN),最终得分的平均值创建了一个强大的预测模型.

主要成果:

  • 提出的基于MDEN的模型与现有方法相比,表现优越.
  • 在MDEN计划中,相比RAN,RNN,LSTM和1DCNN,MDEN计划实现了0.79%,2.98%,2.21%和1.40%的改进.
  • 该系统有效地最大限度地降低了错误率,并通过优化特征加权来提高预测准确性.

结论:

  • 开发的基于DL的MDEN系统在预测患者对癌症治疗的反应方面取得了重大进展.
  • 这种方法有可能个性化放射治疗和化疗,从而改善患者的治疗结果并减少不良影响.
  • 该研究强调了集成先进的DL技术和优化算法的有效性,以提供强大的临床决策支持.