Research Resource for Complex Physiologic Signals

Physionet成立于1999年，是由NIH赞助的复杂生理信号研究资源， 已经在生物医学数据和软件资源中获得了卓越的地位。它的数据档案是第一个， 并且仍然是世界上最大、最全面和最广泛使用的时变生理学信息库 信号。它的软件集合支持对其自身和其他数据库的探索和定量分析 通过提供各种文档齐全、经过严格测试的开源程序，这些程序可以在任何 站台。Physionet的研究团队推动创建和丰富：i)数据收集，提供 长时间内对病理生理学的全面、多方面的看法，如MIMIC(医学 重症监护信息集市)重症监护病人数据库；ii)量化的分析方法 与风险分层和健康状况评估相关的生理信号中编码的信息；iii)用户 增加价值和改善对资源数据的访问的接口、参考材料和服务 软件；以及iv)专注于高优先级临床问题的独特年度挑战，例如早期预测 从单导联心电图(ECG)中检测和量化脓毒症的睡眠呼吸暂停综合征， 重症监护病房(ICU)中的错误警报检测、持续胎儿心电监测和阵发性心房 纤颤的检测和预测。Physionet是经过验证的创新研究的推动者和加速器 具有不同兴趣范围的调查人员，致力于通过其他数据实现的项目 无法接近。PhysioNet的创建和发展被授予2016年度最高荣誉 医疗器械促进会(AAMI)。Physionet在世界范围内，不断增长 由研究人员、临床医生、教育工作者、受训人员以及医疗器械和软件开发人员组成的社区 每天检索约380 GB的数据，平均每年发表近300篇新的学术文章。完毕 未来五年，我们的目标是：1)利用新数据和技术增强PhysioNet的影响力；2)开发新的 量化与健康状况评估相关的生理信号中的动态信息的方法，并用于 急性和慢性风险分层，以及3)通过我们的国际 解决关键临床问题的挑战和新的数据注释倡议。

PhysioNet, established in 1999 as the NIH-sponsored Research Resource for Complex Physiologic Signals, has attained a preeminent status among biomedical data and software resources. Its data archive was the first, and remains the world's largest, most comprehensive and widely used repository of time-varying physiologic signals. Its software collection supports exploration and quantitative analyses of its own and other databases by providing a wide range of well-documented, rigorously tested open-source programs that can be run on any platform. PhysioNet's team of researchers drive the creation and enrichment of: i) Data collections that provide comprehensive, multifaceted views of pathophysiology over long time intervals, such as the MIMIC (Medical Information Mart for Intensive Care) Databases of critical care patients; ii) Analytic methods for quantification of information encoded in physiologic signals relevant to risk stratification and health status assessment; iii) User interfaces, reference materials and services that add value and improve access to the resource’s data and software; and iv) unique annual Challenges focusing on high priority clinical problems, such as early prediction of sepsis, detection and quantification of sleep apnea syndromes from a single lead electrocardiogram (ECG), false alarm detection in the intensive care unit (ICU), continuous fetal ECG monitoring, and paroxysmal atrial fibrillation detection and prediction. PhysioNet is a proven enabler and accelerator of innovative research by investigators with a diverse range of interests, working on projects made possible by data that are otherwise inaccessible. The creation and development of PhysioNet were recognized with the 2016 highest honor of the Association for the Advancement of Medical Instrumentation (AAMI). PhysioNet's world-wide, growing community of researchers, clinicians, educators, trainees, and medical instrument and software developers retrieve about 380 GB of data per day and publish a yearly average of nearly 300 new scholarly articles. Over the next five years we aim to: 1) Enhance PhysioNet’s impact with new data and technology; 2) Develop new methods to quantify dynamical information in physiologic signals relevant for health status assessment, and for acute and chronic risk stratification, and 3) Harness the research community through our international Challenges that address key clinical problems and a new data annotation initiative.