Novel Deep Learning Tools for Clinical Decision Support in Postoperative Pain Management

摘要 术后疼痛(POP)给数百万美国人带来了负担，美国为此付出了数千亿美元的代价 每年的医疗保健系统。管理不善的急性POP通常会导致发病率、死亡率和许多 其他并发症，如慢性POP和阿片类药物过度使用。对POP结果的准确预测和深入 了解POP的致病机制是发展有效的POP管理的关键。此外，许多流行音乐 研究表明，对麻醉方法和术后药物使用的反应不同，提示 迫切需要有效的方法来准确识别患者亚组，以便更有效地进行POP管理 根据个别病人的需要量身定做。然而，实现这些目标是具有挑战性的，因为 POP机制和来自理想的大型随机对照试验的有限数据。另一方面，丰富的 在手术患者的电子健康记录(EHR)中发现的观察性POP数据很容易获得，并且 它们可以作为一种具有成本效益的替代方案，以应对民研计划管理方面的重大挑战。 然而，POP的病因是错综复杂的，即多种因素可能相互交织，影响POP 结果是非线性和非相加的，带来了令人生畏的建模挑战。此外，令人困惑的是， 与观测数据相关的一个主要问题是，对因果关系的研究是一个特殊的挑战 对POP数据的分析。此外，流行音乐的结果，如流行音乐强度分数，通常是不规律的和重复的 使用两个截然不同的数据流程进行测量和非正态分布，需要更高级的分析 方法：研究方法。该提案旨在用最先进的深度解决这些分析和建模挑战 改进POP管理的学习方法。具体地说，我们将1)建立稳健的深度学习模型fi 更准确地预测急性和慢性POP，以实现及时的POP控制和护理；2)制定有效的POP 基于深度学习的半参数方法识别POP设计的真正原因和机制 更有效的POP管理干预；以及3)建立强大的模型，以管理稳健的隐藏子群体 分析，以开发最优的POP管理，以适应个别患者的需求。已开发的方法 在AIMS 1中，3人受到激励，并将通过两个独特的数据进行测试：来自北方大学的大型EHR数据 卡罗莱纳在教堂山的卡罗莱纳健康数据仓库(CDW-H)，以及来自NIH的高质量队列数据- 资助的临时性术后疼痛特征研究，在规模和范围上补充了CDW-H。这个 该项目将阐明民研计划机制的科学基础，并提供改进的民研计划管理。fic。

Abstract Postoperative pain (POP) burdens millions of Americans, and it costs hundreds of billions dollars to the US healthcare system annually. Poorly managed acute POP often leads to increased morbidity, mortality, and many other complications, such as chronic POP and opioid overuse. Accurate prediction of POP outcomes and in-depth understandings of causal mechanisms of POP is critical to develop effective POP management. Also, many POP studies indicate heterogeneity of responses to anesthesia methods and postoperative substance use, suggesting a critical need for effective methods to accurately identify patient subgroups for more effective POP management tailored to the individual patient's needs. However, achieving these goals is challenging due to the complex POP mechanisms and limited data from ideal large randomized controlled trials. On the other hand, abundant observational POP data found in surgery patients' electronic health records (EHRs) are readily available, and they can serve as a cost-effective alternative to address the critical challenges in POP management. However, the etiology of POP is intricate, i.e. many factors may interweave with each other and impact POP outcomes non-linearly and non-additively, introducing daunting modeling challenges. Furthermore, confounding, a major concern associated with observational data, represents a particular challenge for conducting causal analysis on POP data. Also, POP outcomes such as POP intensity scores are often irregularly and repeatedly measured, and distributed non-normally with two distinct data processes, requiring more advanced analysis methods. This proposal aims to overcome these analytic and modeling challenges with state-of-the-art deep learning methods to improve POP management. Specifically, we will 1) establish robust deep learning models for more accurate predictions of both acute and chronic POP to achieve timely POP control and care; 2) develop valid deep learning based semi-parametric methods to identify true causal factors and mechanisms of POP to design more effective POP management interventions; and 3) build powerful models to conduct robust hidden subgroup analysis to develop the optimal POP management tailored to the individual patient's needs. Methods developed in Aims 1 3 are motivated and will be tested by two unique data: a large EHR data from the University of North Carolina at Chapel Hill's Carolina Data Warehouse for Health (CDW-H), and a high-quality cohort data from NIH- funded TEMporal PostOperative Pain Signatures study, which complements the CDW-H in scale and scope. The project will elucidate the scientific underpinnings of POP mechanisms and provide improved POP management.