scholarly journals Enhancing Biosurveillance Specificity Using PraedicoTM, A Next Generation Application

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Alireza Vahdatpour ◽  
Cynthia A. Lucero-Obusan ◽  
Chris Lee ◽  
Gina Oda ◽  
Patricia Schirmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Next Generation ◽  
Community Based ◽  
Gradual Improvement ◽  
Detection Algorithms ◽  
Multiple Detection ◽  
Higher Sensitivity

 We evaluated the specificity of Praedico Biosurveillance, a next generation biosurveillance application leveraging multiple detection algorithms, big data and machine learning, for VA outpatient syndromic surveillance alerting during the period of June 2014 thru May 2015, and compared it to the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). Praedicoâ„¢ Biosurveillance generated alerts were significantly lower compared to ESSENCE generated alerts across all major syndromic syndromes and demonstrated higher sensitivity to seasons (i.e., ILI activity in winter). Reducing alerting fatigue would enhance specificity of computer-generated alerts, promoting more usage and gradual improvement in the algorithm's output.

scholarly journals Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting

2020 ◽  
Vol 26 (9) ◽  
pp. 2196-2200
Author(s):  
Emily Alsentzer ◽  
Sarah-Blythe Ballard ◽  
Joan Neyra ◽  
Delphis M. Vera ◽  
Victor B. Osorio ◽  
...  
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Resource Limited Setting ◽  
Outbreak Detection ◽  
Syndromic Surveillance System ◽  
Detection Algorithms ◽  
Resource Limited ◽  
Limited Setting

scholarly journals Linking syndromic surveillance with virological self-sampling

2007 ◽  
Vol 136 (2) ◽  
pp. 222-224 ◽  
Author(s):  
D. L. COOPER ◽  
G. E. SMITH ◽  
F. CHINEMANA ◽  
C. JOSEPH ◽  
P. LOVERIDGE ◽  
...  
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Influenza A ◽  
Influenza B ◽  
Community Based ◽  
Syndromic Surveillance System ◽  
Influenza A H1n1 ◽  
The Mean ◽  
Nhs Direct ◽  
Mean Time

SUMMARYCalls to a UK national telephone health helpline (NHS Direct) have been used for syndromic surveillance, aiming to provide early warning of rises in community morbidity. We investigated whether self-sampling by NHS Direct callers could provide viable samples for influenza culture. We recruited 294 NHS Direct callers and sent them self-sampling kits. Callers were asked to take a swab from each nostril and post them to the laboratory. Forty-two per cent of the samples were returned, 16·2% were positive on PCR for influenza (16 influenza A(H3N2), three influenza A (H1N1), four influenza B) and eight for RSV (5·6%). The mean time between the NHS Direct call and laboratory analysis was 7·4 days. These samples provided amongst the earliest influenza reports of the season, detected multiple influenza strains, and augmented a national syndromic surveillance system. Self-sampling is a feasible method of enhancing community-based surveillance programmes for detection of influenza.

scholarly journals Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting

2020 ◽  
Vol 26 (9) ◽  
pp. 2196-2200
Author(s):  
Emily Alsentzer ◽  
Sarah-Blythe Ballard ◽  
Joan Neyra ◽  
Delphis M. Vera ◽  
Victor B. Osorio ◽  
...  
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Resource Limited Setting ◽  
Outbreak Detection ◽  
Syndromic Surveillance System ◽  
Detection Algorithms ◽  
Resource Limited ◽  
Limited Setting

scholarly journals Machine learning based distributed big data analysis framework for next generation web in IoT

2021 ◽  
Vol 18 (2) ◽  
pp. 597-618
Author(s):  
Sushil Singh ◽  
Jeonghun Cha ◽  
Tae Kim ◽  
Jong Park
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Cloud Layer ◽  
Analysis Framework ◽  
Next Generation ◽  
Distributed Framework ◽  
Data Scaling

For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.

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