Deep Neural Networks To Treat Atrial Fibrillation

深度神经网络治疗心房颤动

基本信息

批准号：
10470132
负责人：
Tina Baykaner
金额：
$ 19.35万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10470132
关键词：
Ablation Address Adult Affect Anti-Arrhythmia Agents Arrhythmia Atrial Fibrillation Award Biochemical Biometry Cardiac Classification Clinical Clinical Research Computer Models Computing Methodologies Data Data Element Data Set Disease Economic Burden Electrophysiology (science)Epidemic Failure Fatigue Flecainide Freedom Funding Health Heart Atrium Heart failure Individual Judgment Lead Life Style Modification Machine Learning Medicine Mentored Patient-Oriented Research Career Development Award Mentorship Methodology Modification Morbidity - disease rate Nonpharmacologic Therapy Outcome Patients Persons Pharmaceutical Preparations Pharmacology Pharmacotherapy Phenotype Physicians Positioning Attribute Pulmonary veins Registries Research Design Research Personnel Risk Risk Factors Scientist Signal Transduction Stroke Structure Supervision Syndrome Taxonomy Techniques Testing Therapeutic Training Translating Triage Work clinical center clinical phenotype clinical predictors clinical risk cohort cost deep neural network design diverse data dofetilide economic implication experience experimental study falls functional outcomes heart rhythm imaging study individualized medicine insight machine learning algorithm machine learning method machine learning model machine learning prediction mortality neural network novel patient oriented research personalized medicine predictive test prevent prospective responders and non-responders response skills success supervised learning tool unsupervised learning

项目摘要

PROJECT SUMMARY Atrial fibrillation (AF) is a major health problem affecting over 5 million people in the US leading to significant morbidity and even mortality. Therapy for this epidemic is suboptimal, with success of 30-70% at 1 year for most therapies. Despite great advances in understanding potential AF mechanisms, these insights have not yet translated into better AF therapy. The scientific focus of the project centers on the issue of identifying novel phenotypes for the heterogeneous conditions that currently fall under the rubric of AF. Machine learning is an approach well-suited to identify novel classifications from large diverse data sets that are traditionally difficult to separate. I will use machine learning and computational methods to analyze detailed clinical, structural, cardiac electrophysiological and biochemical features in patients with AF, to better predict responders and non-responders to various therapies. This may enable prospective guidance to tailor personalized therapy. In performing this project, I will grow as a physician-scientist focused on patient-oriented research in atrial fibrillation. The specific aims of the scientific project are as follows: First, I will create a novel disease taxonomy for AF that classifies patients successfully treated by risk factor modification, antiarrhythmic drug therapy, or diverse approaches to ablation, using computational methods and supervised learning on large training data from my collaborators. I will assess the predictive efficacy of these disease partitions in a testing cohort of patients referred for treatment of AF. Second, I will use advanced techniques in machine learning and patient-level analyses to explain why a certain strategy may fail or succeed in an individual, paving the way for clinical use. Third, in a pilot prospective clinical study, I will assess the feasibility and accuracy of these machine learning models. The findings from these experiments may provide an immediate clinical impact by delivering AF therapy options in a patient-specific manner that optimizes benefit while reducing risk. In addition, under the balanced and expert mentorship provided by this award, I will gain the necessary computational modelling, clinical research design and biostatistical methodology experience to design comprehensive studies and be competitive for independent funding.

项目概要心房颤动 (AF) 是影响美国超过 500 万人的主要健康问题，导致重大健康问题发病率甚至死亡率。这种流行病的治疗效果不佳，一年内的成功率为 30-70% 大多数疗法。尽管在理解潜在房颤机制方面取得了巨大进展，但这些见解并没有但却转化为更好的 AF 治疗。该项目的科学重点集中在识别异质性的新表型的问题上。目前属于 AF 范畴的条件。机器学习是一种非常适合识别从传统上难以分离的大型多样化数据集中进行新的分类。我会用机器学习和计算方法来分析详细的临床、结构、心脏电生理和 AF 患者的生化特征，以更好地预测对各种治疗的反应者和无反应者。这可能有助于制定个性化治疗的前瞻性指导。在执行这个项目的过程中，我将成长为一个医生科学家专注于以患者为导向的心房颤动研究。该科学项目的具体目标如下：首先，我将为 AF 创建一个新的疾病分类法对通过危险因素修正、抗心律失常药物治疗或多种治疗成功治疗的患者进行分类消融方法，使用计算方法和对我的大量训练数据进行监督学习合作者。我将在一组测试患者中评估这些疾病分区的预测功效转诊治疗 AF。其次，我将使用机器学习和患者层面的先进技术分析以解释为什么某种策略在个体中可能失败或成功，为临床应用铺平道路。第三，在试点前瞻性临床研究中，我将评估这些机器学习的可行性和准确性模型。这些实验的结果可能会通过房颤治疗产生直接的临床影响以针对患者特定的方式进行选择，在降低风险的同时优化效益。另外，在均衡条件下以及该奖项提供的专家指导，我将获得必要的计算建模、临床研究设计和生物统计方法经验来设计全面的研究并成为独立资助具有竞争力。