Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to ‘black box’ machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

数据驱动的疾病进展模型是一组新兴的计算工具，可以重建长期慢性疾病的疾病时间表，为疾病过程及其潜在机制提供独特的见解。此类方法将先验的人类知识和假设与大规模数据处理和参数估计相结合，从短期数据推断长期疾病轨迹。与“黑匣子”机器学习工具相比，数据驱动的疾病进展模型通常需要较少的数据，并且本质上是可解释的，因此除了能够进行分类、预测和分层之外，还有助于理解疾病。在这篇综述中，我们将当前数据驱动的疾病进展模型置于一个总体框架中，并讨论与构建静态疾病概况的更广泛的机器学习工具相比，它们在构建疾病时间表方面的增强实用性。我们回顾了他们在多种神经退行性疾病（特别是阿尔茨海默病）中所获得的见解，其应用包括确定疾病生物标志物的时间轨迹、测试有关疾病机制的假设和揭示疾病亚型。我们概述了技术开发和转化为更广泛的神经科学和非神经科学应用的关键领域。最后，我们讨论了将疾病进展模型整合到临床实践和试验环境中的潜在途径和障碍。