Predictive Models for Opioid Use Disorder Using Genomic, Social, and Clinical Factors

使用基因组、社会和临床因素的阿片类药物使用障碍的预测模型

基本信息

批准号：
10797165
负责人：
Rodney Allanigue Gabriel
金额：
$ 19.75万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10797165
关键词：
Adult Affect Age Body mass index Chronic Classification Clinical Clinical Data Code Communities Control Groups Copy Number Polymorphism Data Data Set Databases Dependence Development Diagnosis Enrollment Ensure Equity Ethnic Origin Exposure to Family Fibromyalgia Future Gender Genetic Markers Genomics Geography Individual Machine Learning Medical Metadata Methods Minority Modeling Outcome Output Pain Patients Performance Persons Pharmaceutical Preparations Population Population Heterogeneity Populations at Risk Postoperative Pain Prevalence Procedures Public Health Race Recording of previous events Research Risk Sampling Single Nucleotide Polymorphism Social Environment Socioeconomic Status Substance Use Disorder Techniques Translations Underrepresented Populations United States Variant Visualization addiction chronic pain chronic painful condition clinical care clinical decision support clinically relevant genome sequencing genome wide association study genomic biomarker genomic data genomic profiles health equality health equity high risk illicit opioid improved insight interest machine learning model machine learning prediction multiple data types neural network novel novel marker opioid abuse opioid epidemic opioid mortality opioid therapy opioid use opioid use disorder outcome prediction painful neuropathy patient population patient stratification predictive modeling prescription opioid prescription pain reliever risk prediction model risk stratification sex social social factors time use tool vector whole genome

项目摘要

PROJECT SUMMARY / ABSTRACT The opioid crisis is a major public health problem in the United States. Over the past two decades, opioid use and abuse have increased dramatically, with over 5 million people in the United States using prescription analgesics without medical need or prescription. This has resulted in a significant increase in opioid-related deaths and addiction rates, with the crisis having a profound impact on individuals, families, and communities. The proposal aims to develop machine learning-based predictive models for opioid use disorder (OUD) leveraging genomic, social, and clinical factors. The project will utilize the diverse and equitable AllOfUs database to identify novel genomic markers associated with OUD in patients with and without co-existing pain conditions. A significant advantage of the AllOfUs database is the diversity of the patient population and clinical samples – over 50% of the population is considered underrepresented. This will be achieved through genome- wide association analysis to identify novel single nucleotide variants, copy number variants, and/or structural variants. The project will also use machine learning techniques to develop predictive models that classify the risk of OUD, integrating various data types such as clinical factors, social factors, and genomic data. The project aims to identify key features that aid in the development of improved models for predicting the risk of OUD. The first specific aim of the proposal is to identify associations between genomic profiles and OUD. The project will focus on patients with or without co-existing pain conditions and identify novel genetic markers associated with OUD in each of these unique patient populations. The second specific aim is to develop predictive models using machine learning techniques to classify the risk of OUD. The models will integrate social, clinical, and genomic data to provide clinicians with a tool to risk stratify their patients. The project aims to develop robust machine learning-based models predicting OUD and visualize the individual features' impacts on model performance to provide understanding of which factors are most impactful to predicting the outcome.

项目摘要 /摘要阿片类药物危机在美国是一个主要的公共卫生问题。虐待急剧增加，有超过500万的偷窥状态使用处方无需医疗或处方的镇痛药导致阿片类药物相关相关相关相关相关的相关相关的镇痛药显着增加。死亡和成瘾率，危机对个人，家庭和社区产生了深远的影响。该提案旨在开发基于机器学习的阿片类药物使用障碍（OUD）的预测模型利用基因组，社会和临床因素。数据库以识别患有及共存疼痛患者的Oud Oud相关的新型基因组标记条件。样品 - 超过50％的人口被认为是不受欢迎的。广泛的关联分析，以识别Novy Novy核苷酸变体，拷贝数变化和/或结构该项目将愿意开发对风险进行分类的预测模型 OUD，整合了各种数据类型，例如临床因素，社会因素和基因组数据旨在确定有助于开发改进模型以预测风险的关键特征。该提案的第一个具体目的是确定基因组概况与OUD之间的关联将专注于有或没有共同验证疼痛状况的患者，并确定被刺激的Novy Novy标记在每个独特的患者人群中都有OUD。第二个具体目的是使用机器学习技术开发预测模型以对风险进行分类 OUD的模型将整合社会，临床和基因组数据他们的病人。该项目旨在开发基于机器学习的强大模型，以预测OUD并可视化个人功能对模型性能的影响，以了解哪些因素最重要的因素预测结果。