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.

数字健康技术，如可穿戴设备，通过个性化、连续和实时监测患者，为新型疗法和个体化医疗的发展做出了贡献。本文提出了一种新颖的药理学启发的神经随机微分方程模型，可以应对从这些技术中产生的具有随机特性的数据进行分析的挑战。使用合成数据，我们证明了我们的方法在识别治疗效果和学习因果关系方面是有效的，从而实现反事实模拟。

从噪声到信号：通过药理学引导的神经 SDE 揭示数字健康数据中的治疗效果

From Noise to Signal: Unveiling Treatment Effects from Digital Health  Data through Pharmacology-Informed Neural-SDE

This document provides responses to the FDA's request for public comments
(Docket No FDA 2023 N 4853) on the role of digital health technologies (DHTs)
in detecting prediabetes and undiagnosed type 2 diabetes. It explores current
DHT applications in prevention, detection, treatment and reversal of
prediabetes, highlighting AI chatbots, online forums, wearables and mobile
apps. The methods employed by DHTs to capture health signals like glucose,
diet, symptoms and community insights are outlined. Key subpopulations that
could benefit most from remote screening tools include rural residents,
minority groups, high-risk individuals and those with limited healthcare
access. Capturable high-impact risk factors encompass glycemic variability,
cardiovascular parameters, respiratory health, blood biomarkers and patient
reported symptoms. An array of non-invasive monitoring tools are discussed,
although further research into their accuracy for diverse groups is warranted.
Extensive health datasets providing immense opportunities for AI and ML based
risk modeling are presented. Promising techniques leveraging EHRs, imaging,
wearables and surveys to enhance screening through AI and ML algorithms are
showcased. Analysis of social media and streaming data further allows disease
prediction across populations. Ongoing innovation focused on inclusivity and
accessibility is highlighted as pivotal in unlocking DHTs potential for
transforming prediabetes and diabetes prevention and care.

该研究总结了数字健康技术在预防、检测和治疗潜在糖尿病和未经确诊的 2 型糖尿病方面的应用，重点介绍了 AI 聊天机器人、在线论坛、可穿戴设备和移动应用。此外，还探讨了通过 AI 和机器学习算法提高筛查效果的技术以及通过分析社交媒体和流媒体数据进行人群疾病预测的方法。