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相关概念视频

Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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相关实验视频

Updated: May 27, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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在多发性硬化症中,可解释的进展时间预测.

Robbe D'hondt1, Klest Dedja1, Sofie Aerts2

  • 1KU Leuven, Dept. Public Health and Primary Care, Kortrijk, Belgium; itec, imec research group at KU Leuven, Kortrijk, Belgium.

Computer methods and programs in biomedicine
|February 18, 2025
PubMed
概括
此摘要是机器生成的。

这项研究使用可解释的机器学习来模拟多发性硬化症残疾进展的时间,比传统的二进制模型提供更个性化的患者预后. 它实现了最先进的预测,并提供了临床上有效的见解.

关键词:
残疾进展的进展情况.可解释的人工智能纵向数据 纵向数据 纵向数据多发性硬化症是多发性硬化症.对生存分析的分析.

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The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
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相关实验视频

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The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
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科学领域:

  • 机器学习在神经学中的应用.
  • 临床研究中的生存分析.
  • 在医疗保健中的可解释的人工智能

背景情况:

  • 目前的多发性硬化症 (MS) 预后模型使用二元预测,未能考虑个体疾病的严重程度.
  • 在MS研究中需要更细致的预后工具.

研究的目的:

  • 为了建模MS患者残疾进展的时间.
  • 通过可解释的机器学习技术来提高模型的解释性.

主要方法:

  • 利用MSBase注册表中的29,201名患者的一个子集.
  • 采用随机生存森林来预测残疾进展的时间到事件 (扩展残疾状况尺度).
  • 应用了SHAP和Bellatrex以实现模型可解释性,提供全球和本地见解.

主要成果:

  • 随机生存森林实现了最先进的性能,与之前的随机森林模型相比.
  • 该模型准确地预测了10年间的进展 (AUROC>60%).
  • 可解释性技术揭示了临床相关的见解,例如最近MS治疗对进展率的影响.

结论:

  • 在MS预后中,从二进制分类过渡到时间对事件建模不会影响性能.
  • 可解释的人工智能对于理解和验证MS护理中的预后模型至关重要.
  • 这种方法允许更全面和个性化的患者预后.