人工智能用于痴呆症 药物发现和试验优化优化
Thomas Doherty1,2, Zhi Yao3, Ahmad A L Khleifat4
1Eisai Europe Ltd, Hatfield, UK.
概括
机器学习和大数据分析可以加速痴呆症药物发现和临床试验设计. 克服挑战需要采用多学科方法,利用数据驱动的见解来实现治疗进步.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 临床试验 临床试验
背景情况:
- 痴呆症药物发现和临床试验设计面临重大障碍,原因是患者的变化,疾病持续时间和目标可访问性.
- 历史上的挑战减缓了神经退行性疾病的治疗进展.
研究的目的:
- 审查机器学习 (ML) 在痴呆症药物发现和临床试验设计中的当前应用.
- 确定机会并提出建议,以克服实施障碍.
主要方法:
- 审查关于ML在药物发现和临床试验设计中的应用现有文献.
- 分析大数据分析在解决治疗挑战方面的潜力.
主要成果:
- 机器学习和大数据有可能通过改进目标识别和试验设计来加速痴呆症治疗的发展.
- 大量的医疗数据集使得对关键的临床和治疗问题的数据驱动洞察力成为可能.
结论:
- 多学科的方法对于在痴呆症研究中有效的数据驱动决策至关重要.
- 应对当前的挑战将释放ML和大数据在痴呆症药物发现和临床试验中的全部潜力.
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