从噪声到信号：通过药理学启发的神经SDE从数字健康数据中揭示治疗效应
From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE
AI总结：
针对数字健康数据随机性强、有缺失、个体差异大难以用传统方法分析的问题，提出药理学启发的神经SDE模型，经合成数据验证可有效识别治疗效应、学习因果关系并支持反事实模拟。
摘要：
可穿戴设备等数字健康技术（DHT）可对患者进行个性化、持续的实时监测，正推动新型疗法与个性化医疗的发展。要从这类技术中挖掘洞见，需采用合适的建模技术捕捉疾病状态的临床相关变化。这类设备生成的数据本质具有随机性，可能存在缺失项，且个体间差异显著，因此难以用传统纵向建模技术分析。我们提出一种新型的药理学启发神经随机微分方程（SDE）模型，可应对上述挑战。通过合成数据实验，我们证明该方法能有效识别治疗效应，并从随机数据中学习因果关系，从而支持反事实模拟。
英文摘要：
Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the development of novel therapies and personalized medicine. Gaining insight from these technologies requires appropriate modeling techniques to capture clinically-relevant changes in disease state. The data generated from these devices is characterized by being stochastic in nature, may have missing elements, and exhibits considerable inter-individual variability - thereby making it difficult to analyze using traditional longitudinal modeling techniques. We present a novel pharmacology-informed neural stochastic differential equation (SDE) model capable of addressing these challenges. Using synthetic data, we demonstrate that our approach is effective in identifying treatment effects and learning causal relationships from stochastic data, thereby enabling counterfactual simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
