Deep Neural Networks To Treat Atrial Fibrillation

项目摘要 心房纤颤（AF）是影响美国超过500万人的主要健康问题，导致严重的心血管疾病。 发病率甚至死亡率。这种流行病的治疗是次优的，1年的成功率为30-70%， 大多数治疗尽管在理解潜在的AF机制方面取得了很大进展，但这些见解还没有 却转化为更好的房颤治疗。 该项目的科学重点是确定异质性的新表型的问题。 机器学习是一种非常适合识别AF的方法。 从传统上难以分离的大的不同的数据集的新的分类。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，以更好地预测各种治疗的应答者和非应答者。 这可以实现前瞻性指导以定制个性化治疗。在执行这个项目时，我将成长为一个 心房颤动的治疗方法有哪些？ 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 将通过风险因素调整、抗肿瘤药物治疗或多种治疗方法成功治疗的患者分类， 消融的方法，使用计算方法和监督学习的大型训练数据，从我的 合作者我将评估这些疾病分区在测试患者队列中的预测功效 其次，我将使用机器学习和患者水平的先进技术， 分析解释为什么某种策略在个体中可能失败或成功，为临床使用铺平道路。 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 模型 这些实验的结果可能通过提供AF治疗提供直接的临床影响 以患者特定的方式提供选择，在降低风险的同时优化获益。此外，在平衡 和专家指导提供了这个奖项，我将获得必要的计算建模，临床 研究设计和生物统计方法的经验，设计全面的研究， 竞争独立融资。

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.