Deep Neural Networks To Treat Atrial Fibrillation

深度神经网络治疗心房颤动

• 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
• 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

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.

项目摘要 心房颤动（AF）是一个主要的健康问题，影响了美国超过500万人 发病率甚至死亡率。该流行病的治疗是次优的，在1年时成功30-70％ 大多数疗法。尽管在理解潜在的AF机制方面取得了巨大进步，但这些见解却没有 但是被转化为更好的AF疗法。 该项目的科学重点集中于确定异质表型的新表型问题 目前属于AF的条件。机器学习是一种非常适合识别的方法 传统上很难分开的大型数据集中的新分类。我将使用机器 学习和计算方法分析详细的临床，结构，心脏电生理学和 AF患者的生化特征可以更好地预测各种疗法的反应者和无反应者。 这可能使前瞻性指导量身定制个性化治疗。在执行这个项目时，我将成长为 医师科学家专注于以患者为导向的房颤研究。 科学项目的具体目的如下：首先，我将为AF创建一种新颖的疾病分类法 这对通过风险因素修饰，抗心律失常药物治疗或多样的患者进行了分类 消融方法，使用计算方法和从我的大型培训数据上进行监督学习 合作者。我将评估这些疾病分区的预测功效 转介治疗AF。第二，我将在机器学习和患者级别中使用高级技术 分析以解释为什么某个策略可能会失败或成功，从而为临床使用铺平了道路。 第三，在一项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 型号。 这些实验的发现可能通过提供AF治疗来立即产生临床影响 以特定于患者的方式进行选择，可在降低风险的同时优化福利。另外，在平衡下 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学方法论，以设计综合研究并成为 具有独立资金的竞争力。