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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：
10670469
负责人：
Baiming Zou
金额：
$ 42.3万
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/10670469
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 Education Electronic Health Record Etiology Florida Funding Goals Health Healthcare Systems Heterogeneity Image Individual Inpatients Intervention Lead 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 Research Research Personnel Risk Risk Factors Socioeconomic Status Structure Subgroup Techniques Testing Time United States National Institutes of Health Universities analytical tool base 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