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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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针对阿尔茨海默病的多目标优化配方 试验 患者选择

Alireza Moayedikia1, Sara Fin2, Uffe Kock Wiil3

  • 1Swinburne Business School, Swinburne University of Technology, Australia.

Journal of biomedical informatics
|November 17, 2025
PubMed
概括

使用多目标优化优化阿尔茨海默病临床试验资格标准的优化提供了渐进式效率增长. 这种计算方法验证了现有的做法,并提高了招聘的可行性,尽管结果显示了变化.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.临床试验的设计优化成本,优化成本.多目标优化多目标优化患者的选择患者的选择

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科学领域:

  • 计算生物学和生物信息学
  • 临床试验设计和优化
  • 神经科学和阿尔茨海默氏症疾病研究

背景情况:

  • 阿尔茨海默病 (AD) 临床试验面临高屏幕失败率 (>80%),阻碍了进展.
  • 目前的患者选择依赖于专家的共识,缺乏对竞争目标的系统评估.
  • 在AD试验设计中,有必要平衡统计能力,招聘可行性,安全性和成本.

研究的目的:

  • 开发和实施AD临床试验资格标准的多目标优化框架.
  • 系统地确定最佳的标准配置,平衡患者识别准确性,招聘可行性和经济效率.
  • 在AD试验设计中,根据专家的共识验证计算方法.

主要方法:

  • 使用非主导排序遗传算法III (NSGA-III) 进行多目标优化.
  • 雇佣了国家阿尔茨海默氏症协调中心的数据 (2,743名参与者) 与临床和生物标志物信息.
  • 优化了14个符合条件的参数,并使用蒙特卡洛模拟,引导分析和SHAP可解释性进行验证.

主要成果:

  • 确定了11个帕雷托最佳解决方案,平衡F1分数 (0.979-0.995) 和符合条件的患者池 (108-327).
  • 优化标准识别了与标准标准相似的患者队列,但具有潜在的成本节省 (1048美元/患者).
  • 生物标志物要求被确定为占主导地位的成本驱动因素;计算结果与专家设计的标准趋同.

结论:

  • 多目标优化通过系统地验证和概率地提高AD试验的效率来提供增量价值.
  • 计算方法作为复杂的验证工具,在现有框架内确定具体的效率改进.
  • 特定地点的评估和招聘基础设施质量至关重要;优化增强,而不是取代,临床专业知识.