scholarly journals A comparison of methodologies for the real-time identification of hospitalized patients with acute exacerbations of COPD

2019 ◽  
Vol Volume 14 ◽  
pp. 693-698 ◽  
Author(s):  
Parth Shah ◽  
Andrew McWilliams ◽  
Daniel Howard ◽  
Jason Roberge
Keyword(s):  
Real Time ◽  
Hospitalized Patients ◽  
The Real ◽  
Acute Exacerbations ◽  
Real Time Identification

A Strategy for Real-Time Identification of Hospitalized Patients with Heart Failure

2009 ◽  
Vol 35 (12) ◽  
pp. 622-628
Author(s):  
Ellen F. Robinson ◽  
Chris Cooley ◽  
Astrid B. Schreuder ◽  
Anneliese M. Schleyer ◽  
Robert Birchard ◽  
...  
Keyword(s):  
Heart Failure ◽  
Real Time ◽  
Hospitalized Patients ◽  
Patients With Heart Failure ◽  
Real Time Identification

Electro-acoustic sensor for the real-time identification of the bacteriophages

Talanta ◽  
2018 ◽  
Vol 178 ◽  
pp. 743-750 ◽  
Author(s):  
О.I. Guliy ◽  
B.D. Zaitsev ◽  
I.A. Borodina ◽  
А.М. Shikhabudinov ◽  
S.А. Staroverov ◽  
...  
Keyword(s):  
Real Time ◽  
Acoustic Sensor ◽  
The Real ◽  
Real Time Identification

scholarly journals OS3.6 Optical Biopsies in Neurosurgery: Raman Spectroscopy for the Real-time Identification of Tumours during Surgery

2016 ◽  
Vol 18 (suppl_4) ◽  
pp. iv8-iv8 ◽  
Author(s):  
B. Vaqas ◽  
M. Short ◽  
I. Patel ◽  
U. Faiz ◽  
H. Zeng ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Real Time ◽  
The Real ◽  
Real Time Identification

A novel liquid crystal-based sensor for the real-time identification of organophosphonate vapors

2011 ◽  
Vol 160 (1) ◽  
pp. 929-935 ◽  
Author(s):  
Pu-Hong Wang ◽  
Jian-Hua Yu ◽  
Ya-Bin Zhao ◽  
Zhi-Jun Li ◽  
Guang-Qin Li
Keyword(s):  
Liquid Crystal ◽  
Real Time ◽  
The Real ◽  
Real Time Identification

First Application of Cellular Nonlinear Network Methods to the Real-Time Identification of Hot Spots in JET

2009 ◽  
Vol 37 (1) ◽  
pp. 146-152 ◽  
Author(s):  
Guido Vagliasindi ◽  
Andrea Murari ◽  
Paolo Arena ◽  
Luigi Fortuna ◽  
Gilles Arnoux ◽  
...  
Keyword(s):  
Real Time ◽  
Hot Spots ◽  
The Real ◽  
Nonlinear Network ◽  
Network Methods ◽  
Real Time Identification

scholarly journals SEISMICITY CRITERIA FOR THE REAL-TIME IDENTIFICATION OF FORESHOCKS IN THE CORINTH GULF

2004 ◽  
Vol 36 (3) ◽  
pp. 1451 ◽  
Author(s):  
K. Orfanogiannaki ◽  
G. A. Papadopoulos
Keyword(s):  
Real Time ◽  
Time Interval ◽  
Crucial Importance ◽  
The Real ◽  
Corinth Gulf ◽  
Gulf Of Corinth ◽  
Real Time Identification ◽  
Precursory Phenomena ◽  
Time Evaluation

In the Corinth Gulf foreshock sequences occur as a rule within a time interval no longer than four months before the mainshock. If these precursory phenomena could be detected, then it would be utilized for the prediction of the mainshock. However, frequent swarms also characterize the Gulf of Corinth. Therefore, in a real time evaluation, the discrimination between swarms and foreshock sequences is of crucial importance. In this study we focus on establishing seismicity criteria to achieve such discrimination.

Real-Time Spatiotemporal Analysis of Microepidemics of Influenza and COVID-19 Based on Hospital Network Data: Colocalization of Neighborhood-Level Hotspots

2020 ◽  
Vol 110 (12) ◽  
pp. 1817-1824
Author(s):  
Evangelia K. Mylona ◽  
Fadi Shehadeh ◽  
Markos Kalligeros ◽  
Gregorio Benitez ◽  
Philip A. Chan ◽  
...  
Keyword(s):  
Real Time ◽  
Rhode Island ◽  
Local Governments ◽  
Spatiotemporal Analysis ◽  
Hospital Data ◽  
Hotspot Analysis ◽  
The Real ◽  
Spatiotemporal Trends ◽  
Prevention Interventions ◽  
Real Time Identification

Objectives. To identify spatiotemporal patterns of epidemic spread at the community level. Methods. We extracted influenza cases reported between 2016 and 2019 and COVID-19 cases reported in March and April 2020 from a hospital network in Rhode Island. We performed a spatiotemporal hotspot analysis to simulate a real-time surveillance scenario. Results. We analyzed 6527 laboratory-confirmed influenza cases and identified microepidemics in more than 1100 neighborhoods, and more than half of the neighborhoods that had hotspots in a season became hotspots in the next season. We used data from 731 COVID-19 cases, and we found that a neighborhood was 1.90 times more likely to become a COVID-19 hotspot if it had been an influenza hotspot in 2018 to 2019. Conclusions. The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics. Public Health Implications. As local governments move to reopen the economy and ease physical distancing, the use of historic influenza hotspots could guide early prevention interventions, while the real-time identification of hotspots would enable the implementation of interventions that focus on small-area containment and mitigation.

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