权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

用于术后疼痛管理临床决策支持的新型深度学习工具

基本信息

批准号：
10684876
负责人：
Baiming Zou
金额：
$ 40.91万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-16 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10684876
关键词：
Acute Address Age Algorithms American Analgesics Anesthesia procedures Basic Science Caring Chronic Clinical Cohort Studies Complement Complex Critical Care Data Data Analyses Data Sources Databases Disparity Education Electronic Health Record Etiology Florida Funding Goals Health Healthcare Systems Heterogeneity Image Individual Inpatients Intervention Investigation Length of Stay Measures Mediation Medical Care Costs Methods Modeling Morbidity - disease rate Network-based Neural Network Simulation North Carolina Operative Surgical Procedures Outcome Pain Pain Measurement Pain intensity Pain management Patients Perioperative Pharmaceutical Preparations Physicians Policies Policy Maker Population Postoperative Pain Postoperative Period Process Race Randomized, Controlled Trials Regimen Relaxation Research Research Personnel Risk Risk Factors Socioeconomic Status Structure Subgroup Techniques Testing Time United States National Institutes of Health Universities analytical tool circadian clinical care clinical decision support clinical practice cohort computerized tools convolutional neural network cost cost effective data warehouse deep learning deep learning model deep neural network design experience improved individual patient learning strategy machine learning model mortality novel opioid overuse outcome prediction pain outcome pain score patient subsets personalized medicine response semiparametric sex substance use tool user friendly software

项目摘要

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.

摘要术后疼痛（POP）负担着数百万美国人，它花费了美国数千亿美元医疗保健系统每年。管理不善的急性POP常常导致发病率、死亡率和许多其他疾病的增加。其他并发症，如慢性POP和阿片类药物过度使用。准确预测持久性有机污染物的结果，了解持久性有机污染物的成因机制，是开展有效持久性有机污染物管理的关键。许多POP 研究表明对麻醉方法和术后药物使用的反应具有异质性，提示迫切需要有效的方法来准确识别患者亚组，以实现更有效的POP管理根据患者的个人需求定制。然而，实现这些目标是具有挑战性的，由于复杂的 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，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，它们可以作为一种具有成本效益的替代办法，应对持久性有机污染物管理方面的重大挑战。然而，POP的病因是复杂的，即许多因素可能相互交织并影响POP 非线性和非叠加的结果，引入了令人生畏的建模挑战。此外，令人困惑的是，这是与观测数据相关的一个主要问题，代表了进行因果分析的一个特殊挑战。 POP数据分析。此外，POP结果（如POP强度评分）通常不规则且重复测量，并与两个不同的数据过程非正态分布，需要更先进的分析方法.该提案旨在通过最先进的深度分析来克服这些分析和建模挑战。学习改进POP管理的方法。具体来说，我们将1）建立强大的深度学习模型，更准确地预测急性和慢性持久性有机污染物，以实现及时的持久性有机污染物控制和护理; 2）制定有效的基于深度学习的半参数方法，以识别POP的真正因果因素和机制，更有效的POP管理干预措施;以及3）建立强大的模型来进行强大的隐藏亚组分析，以开发适合个体患者需求的最佳POP管理。开发的方法在目标1 - 3中，我们提出了两个独特的数据，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数据卡罗莱纳在查佩尔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TEMporal术后疼痛特征研究，在规模和范围上补充了CDW-H。的项目将阐明持久性有机污染物机制的科学基础，并提供更好的持久性有机污染物管理。