Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, including those driven by artificial intelligence, are rapidly expanding our ability to quantify the different domains of function during rehabilitation, other encounters with healthcare, and in the community. While this seems poised to usher rehabilitation into the era of big data and should be a powerful driver of precision rehabilitation, our field lacks a coherent framework to utilize these data and deliver on this promise. We propose a framework that builds upon multiple existing pillars to fill this gap. Our framework aims to identify the Optimal Dynamic Treatment Regimens (ODTR), or the decision-making strategy that takes in the range of available measurements and biomarkers to identify interventions likely to maximize long-term function. This is achieved by designing and fitting causal models, which extend the Computational Neurorehabilitation framework using tools from causal inference. These causal models can learn from heterogeneous data from different silos, which must include detailed documentation of interventions, such as using the Rehabilitation Treatment Specification System. The models then serve as digital twins of patient recovery trajectories, which can be used to learn the ODTR. Our causal modeling framework also emphasizes quantitatively linking changes across levels of the functioning to ensure that interventions can be precisely selected based on careful measurement of impairments while also being selected to maximize outcomes that are meaningful to patients and stakeholders. We believe this approach can provide a unifying framework to leverage growing big rehabilitation data and AI-powered measurements to produce precision rehabilitation treatments that can improve clinical outcomes.

翻译：精准康复为实现基于证据的个体化康复优化以改善长期功能结局提供了前景。新兴技术，包括人工智能驱动的技术，正迅速扩展我们在康复期间、其他医疗接触场景以及社区环境中量化不同功能领域的能力。尽管这似乎预示着康复即将迈入大数据时代，并应成为推动精准康复的强大驱动力，但本领域仍缺乏一个连贯的框架来利用这些数据并兑现这一承诺。我们提出了一个框架，该框架建立在多个现有支柱之上以填补这一空白。我们的框架旨在识别最优动态治疗方案（Optimal Dynamic Treatment Regimens, ODTR），即一种决策策略，该策略接收一系列可用的测量数据和生物标志物，以识别可能最大化长期功能的干预措施。这是通过设计和拟合因果模型实现的，这些模型利用因果推断工具扩展了计算神经康复框架。这些因果模型能够从不同数据孤岛的异构数据中学习，这些数据必须包含干预措施的详细记录，例如使用康复治疗规范系统。随后，这些模型可作为患者康复轨迹的数字孪生体，用于学习ODTR。我们的因果建模框架还强调定量连接功能不同层级间的变化，以确保干预措施既能基于对损伤的精确测量被精准选择，同时也能被选择以最大化对患者和利益相关者具有意义的结局。我们相信，这种方法可以提供一个统一的框架，以利用日益增长的大规模康复数据和人工智能驱动的测量结果，从而产生能够改善临床结局的精准康复治疗方案。