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Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

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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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人工智能用于痴呆症研究方法优化优化

Magda Bucholc1, Charlotte James2, Ahmad Al Khleifat3

  • 1Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|August 28, 2023
PubMed
概括
此摘要是机器生成的。

人工智能和机器学习显示出对痴呆症研究的希望,但方法上的挑战阻碍了进展. 解决数据共享,可解释性和多样化的数据集等问题对于改善患者的治疗结果至关重要.

关键词:
人工智能的人工智能是人工智能.这是分类分类的分类.临床实用性的临床实用性深度学习是一种深度学习.痴呆症 痴呆症是一种痴呆症.可以概括的概括性.可以解释的解释性.机器学习是机器学习.优化方法 优化方法这是一个回归回归的回归.可复制性的可复制性半监督学习 半监督学习监督学习学习监督学习可以转让的可转让性.没有监督的学习学习.

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科学领域:

  • 计算神经科学是一种计算神经科学.
  • 医疗信息学医学信息学
  • 老年医学 老年医学

背景情况:

  • 人工智能 (AI) 和机器学习 (ML) 越来越多地应用于痴呆症研究.
  • 方法上的挑战限制了从高维数据和临床翻译中获得的见解.
  • 可复制性和通用性是AI/ML对痴呆症的关键问题.

研究的目的:

  • 识别和解决应用人工智能和机器学习在痴呆症研究中的方法挑战.
  • 增强AI和ML的潜力,以改善痴呆症的诊断,预防和管理.
  • 促进在痴呆症护理中的强大,可泛化和可解释的AI/ML模型的最佳实践.

主要方法:

  • 在痴呆症的AI/ML应用中审查和综合当前的挑战.
  • 强调开源代码,数据共享和可解释的建模.
  • 倡导各种数据集和遵守报告准则.

主要成果:

  • 对ML程序的报告不足,妨碍了结果的复制和复制.
  • 在非代表性数据集上训练的模型表现出很差的概括性.
  • 缺乏定义的指标和对可解释性的担忧阻碍了临床采用.

结论:

  • 克服AI/ML的方法挑战对于推进痴呆症研究和护理至关重要.
  • 优先考虑代码/数据共享,可解释性和多样化的数据集将提高模型的稳定性并减少偏差.
  • 为了在人工智能驱动的痴呆症研究中提供清晰度和可重复性,需要标准化的报告准则.