Research Resource for Complex Physiologic Signals

PhysioNet成立于1999年，是NIH赞助的复杂生理信号研究资源， 在生物医学数据和软件资源中取得了卓越的地位。它的数据档案是第一个， 并且仍然是世界上最大的，最全面的和广泛使用的随时间变化的生理数据库。 信号.其软件库支持对其自身和其他数据库的探索和定量分析 通过提供广泛的文档齐全，经过严格测试的开源程序，可以在任何 平台PhysioNet的研究人员团队推动创建和丰富：i）提供 在很长一段时间内，病理生理学的全面，多方面的观点，如MIMIC（医学 重症监护信息市场）重症监护患者的数据库; ii）用于定量 编码在与风险分层和健康状态评估相关的生理信号中的信息; iii）用户 增加价值和改善资源数据访问的接口、参考资料和服务， 软件;以及iv）独特的年度挑战，重点关注高度优先的临床问题，例如早期预测 脓毒症，从单导联心电图（ECG）检测和量化睡眠呼吸暂停综合征， 重症监护室（ICU）中的假警报检测、连续胎儿ECG监测和阵发性心房颤动 纤颤检测和预测。PhysioNet是一个经过验证的创新研究的推动者和加速器， 具有各种兴趣的调查人员，致力于通过数据实现的项目， 难以接近PhysioNet的创建和开发获得了2016年最高荣誉， 美国医疗器械促进协会（AAMI）。PhysioNet在全球范围内， 由研究人员、临床医生、教育工作者、受训人员以及医疗仪器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。超过 未来五年，我们的目标是：1）通过新的数据和技术增强PhysioNet的影响力; 2）开发新的 量化与健康状态评估相关的生理信号中的动态信息的方法， 急性和慢性风险分层，3）通过我们的国际研究社区 解决关键临床问题的挑战和新的数据注释计划。

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.