PhysioNet, established in 1999 as the NIH-sponsored Research Resource for Complex Physiologic Signals, has attained a preeminent status among data and software resources in biomedicine. Its data archive, PhysioBank, was the first, and remains the world's largest, most comprehensive and most widely used repository of time-varying physiologic signals. Its software collection, PhysioToolkit, supports exploration and quantitative analyses of PhysioBank and similar data with a wide range of well-documented, rigorously tested, open-source software that can be run on any platform. PhysioNet's team of researchers leverages results of other funded projects to drive the creation and enrichment of: """""""" Data collections that provide increasingly comprehensive, multifaceted views of pathophysiology over long time intervals, such as the MIMIC II (Multiparameter Monitoring in Intensive Care) Database of critical care patients; """""""" Analytic methods that lead to more timely and accurate diagnoses (such as prediction of acute hypotensive events), and elucidation of dynamical changes associated with disease and aging (such as cardiopulmonary interactions during sleep disordered breathing syndrome); """""""" User interfaces, reference materials and services that add value and improve accessibility to PhysioNet's data and software (such as PhysioNetWorks, a virtual laboratory for data sharing). Impact: PhysioNet is a proven enabler and accelerator of innovative research by specialists, young investigators and trainees alike, working on independent projects and focused biomedical engineering challenges made possible by data that are inaccessible otherwise. Through its PhysioNetWorks, the Resource gives researchers new tools and the opportunity not merely to meet NIH data sharing mandates, but to enrich the data commons with accessible, valuable contributions. By providing free access to its unique and wide- ranging data and software collections, PhysioNet enables studies that lead to an average of 70 scholarly publications per month (well over 5000 studies since its inception by academic, clinical, and industry-affiliated researchers worldwide.
Specific aims : For the next 5 years we aim to: 1. Accelerate PhysioNet's growth with new technology and data; 2. Drive relevant innovation through a vigorous research program on complex physiologic signals; 3. Stimulate and challenge a growing community of investigators.

Public Health Relevance

PhysioNet, the Research Resource for Complex Physiological Signals, maintains the world's largest, most comprehensive, and most widely used repository of time-varying physiological signals and associated signal-processing software, and makes them freely available to the research community. PhysioNet is a proven enabler and accelerator of innovative research by specialists and non- specialists alike, working on independent projects and focused engineering challenges made possible by data that are inaccessible otherwise.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
9R01GM104987-06
Application #
8373058
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Lyster, Peter
Project Start
2007-09-01
Project End
2015-06-30
Budget Start
2012-09-01
Budget End
2013-06-30
Support Year
6
Fiscal Year
2012
Total Cost
$740,704
Indirect Cost
$153,395
Name
Beth Israel Deaconess Medical Center
Department
Type
DUNS #
071723621
City
Boston
State
MA
Country
United States
Zip Code
02215
Zalewski, Aaron; Long, William; Johnson, Alistair E W et al. (2017) Estimating Patient's Health State Using Latent Structure Inferred from Clinical Time Series and Text. IEEE EMBS Int Conf Biomed Health Inform 2017:449-452
Dai, Yang; Lokhandwala, Sharukh; Long, William et al. (2017) Phenotyping Hypotensive Patients in Critical Care Using Hospital Discharge Summaries. IEEE EMBS Int Conf Biomed Health Inform 2017:401-404
Gee, Alan H; Barbieri, Riccardo; Paydarfar, David et al. (2017) Predicting Bradycardia in Preterm Infants Using Point Process Analysis of Heart Rate. IEEE Trans Biomed Eng 64:2300-2308
El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa et al. (2017) ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. Biomed Eng Online 16:26
Costa, Madalena D; Davis, Roger B; Goldberger, Ary L (2017) Heart Rate Fragmentation: A Symbolic Dynamical Approach. Front Physiol 8:827
Goldberger, Ary L; Henriques, Teresa; Mariani, Sara (2016) Sublimation-like Behavior of Cardiac Dynamics in Heart Failure: A Malignant Phase Transition? Complexity 21:24-32
Danziger, John; Chen, Ken P; Lee, Joon et al. (2016) Obesity, Acute Kidney Injury, and Mortality in Critical Illness. Crit Care Med 44:328-34
Johnson, Alistair E W; Ghassemi, Mohammad M; Nemati, Shamim et al. (2016) Machine Learning and Decision Support in Critical Care. Proc IEEE Inst Electr Electron Eng 104:444-466
Schnettler, William T; Goldberger, Ary L; Ralston, Steven J et al. (2016) Complexity analysis of fetal heart rate preceding intrauterine demise. Eur J Obstet Gynecol Reprod Biol 203:286-90
Johnson, Alistair E W; Pollard, Tom J; Shen, Lu et al. (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3:160035

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