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Title: Biologically inspired approaches for biosurveillance anomaly detection and data fusion.

Abstract

This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large- scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets. ACKNOWLEDGEMENTS The authors acknowledge the close collaboration of Scott Lee, Jason Thomas, and Chad Heilig from the US Centers for Disease Control (CDC) in this effort. De-identified biosurveillance data provided by Ken Jeter of the New Mexico Department of Health proved to be an important contribution to our work. Discussions with members of the International Societymore » of Disease Surveillance helped the researchers focus on questions relevant to practicing public health professionals. Funding for this work was provided by Sandia National Laboratories' Laboratory Directed Research and Development program.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1489542
Report Number(s):
SAND2018-14334
671073
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Finley, Patrick D., Levin, Drew, Flanagan, Tatiana Paz, Beyeler, Walter E., Mitchell, Michael David, Ray, Jaideep, Moses, Melanie, and Forrest, Stephanie. Biologically inspired approaches for biosurveillance anomaly detection and data fusion.. United States: N. p., 2018. Web. doi:10.2172/1489542.
Finley, Patrick D., Levin, Drew, Flanagan, Tatiana Paz, Beyeler, Walter E., Mitchell, Michael David, Ray, Jaideep, Moses, Melanie, & Forrest, Stephanie. Biologically inspired approaches for biosurveillance anomaly detection and data fusion.. United States. doi:10.2172/1489542.
Finley, Patrick D., Levin, Drew, Flanagan, Tatiana Paz, Beyeler, Walter E., Mitchell, Michael David, Ray, Jaideep, Moses, Melanie, and Forrest, Stephanie. Sat . "Biologically inspired approaches for biosurveillance anomaly detection and data fusion.". United States. doi:10.2172/1489542. https://www.osti.gov/servlets/purl/1489542.
@article{osti_1489542,
title = {Biologically inspired approaches for biosurveillance anomaly detection and data fusion.},
author = {Finley, Patrick D. and Levin, Drew and Flanagan, Tatiana Paz and Beyeler, Walter E. and Mitchell, Michael David and Ray, Jaideep and Moses, Melanie and Forrest, Stephanie},
abstractNote = {This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large- scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets. ACKNOWLEDGEMENTS The authors acknowledge the close collaboration of Scott Lee, Jason Thomas, and Chad Heilig from the US Centers for Disease Control (CDC) in this effort. De-identified biosurveillance data provided by Ken Jeter of the New Mexico Department of Health proved to be an important contribution to our work. Discussions with members of the International Society of Disease Surveillance helped the researchers focus on questions relevant to practicing public health professionals. Funding for this work was provided by Sandia National Laboratories' Laboratory Directed Research and Development program.},
doi = {10.2172/1489542},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {12}
}