神经退行性疾病进展的数据驱动建模:在黑盒子之外思考
Alexandra L Young1,2, Neil P Oxtoby3, Sara Garbarino4
1UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK. alexandra.young@ucl.ac.uk.
Nature reviews. Neuroscience
|January 8, 2024
概括
数据驱动的疾病进展模型通过从数据中重建时间表,为慢性疾病提供可解释的见解. 这些模型有助于理解,预测和分层,优于静态机器学习方法.
科学领域:
- 计算生物学是一种计算生物学.
- 生物医学信息学是生物医学信息学.
- 神经科学是一个神经科学.
背景情况:
- 数据驱动的疾病进展模型重建慢性疾病的疾病时间表.
- 这些模型将先前的知识与大规模数据处理相结合,以推断疾病轨迹.
- 与"黑子"机器学习不同,这些模型是可解释的,需要更少的数据.
研究的目的:
- 审查和框架数据驱动疾病进展模型的当前景观.
- 与静态机器学习配置文件相比,强调它们在构建疾病时间表中的实用性.
- 讨论这些模型的应用,开发和临床整合.
主要方法:
- 数据驱动的疾病进展模型框架.
- 对神经退行性疾病 (包括阿尔茨海默病) 的应用进行审查.
- 技术发展和翻译途径的分析.
主要成果:
- 数据驱动的模型为疾病时间表的构建提供了增强的实用性.
- 在神经退行性疾病中获得的见解包括生物标志物时间轨迹和亚型发现.
- 这些模型有助于对疾病机制的假设测试.
结论:
- 数据驱动的疾病进展模型为了解慢性疾病提供了可解释和有效的工具.
- 开发的关键领域包括更广泛的神经科学和非神经科学应用.
- 将其纳入临床实践和临床试验需要解决潜在的途径和障碍.
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