TY - GEN
T1 - Negative selection based anomaly detector for multimodal health data
AU - Levin, Drew
AU - Moses, Melanie
AU - Flanagan, Tatiana
AU - Forrest, Stephanie
AU - Finley, Patrick
N1 - Funding Information:
This work was supported by Laboratory Directed Research and Development funding from Sandia National Laboratories. MEM acknowledges the partial support of a James S. McDonnell Foundation Complex Systems Scholar Award. SF acknowledges the partial support of NSF (1518878), DARPA (FA8750-15-C-0118), AFRL (FA8750-15-2-0075), the Sandia National Laboratories Academic Alliance, and the Santa Fe Institute. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energys National Nuclear Security Administration under contract DE-NA0003525.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - Early detection of emerging disease outbreaks is crucial to effective containment and response, yet initial outbreak signatures can be difficult to detect with automated methods. Outbreaks may be masked by noisy data, and signs of an outbreak may be hidden across multiple data feeds. Current biosurveillance methods often perform unimodal statistical analyses that are unable to intelligently leverage multiple correlated data of different types while still retaining quantitative sensitivity. In this paper, we propose and implement an anomaly detection system for health data based upon the human immune system. The adaptive immune system operates over a high-dimensional antigen space in a distributed manner, allowing it to efficiently scale without relying on a centralized controller. Our negative selection algorithm based on the immune system provides effective and scalable distributed anomaly detection for biosurveillance. It detects anomalies in the large, complex data from modern health monitoring data feeds with low false positive rates. Our bootstrap aggregation method improves performance on high-dimensional data sets, and we implement a parallelized version of the algorithm to demonstrate the potential to implement it on a scalable distributed architecture. Our negative selection algorithm is able to detect 90% of all outbreaks with a false positive rate of 11.8% in a publicly available multimodal synthetic health record data set. The scalability and performance of the negative selection algorithm demonstrate that immune computation can provide effective approaches for national and global scale biosurveillence.
AB - Early detection of emerging disease outbreaks is crucial to effective containment and response, yet initial outbreak signatures can be difficult to detect with automated methods. Outbreaks may be masked by noisy data, and signs of an outbreak may be hidden across multiple data feeds. Current biosurveillance methods often perform unimodal statistical analyses that are unable to intelligently leverage multiple correlated data of different types while still retaining quantitative sensitivity. In this paper, we propose and implement an anomaly detection system for health data based upon the human immune system. The adaptive immune system operates over a high-dimensional antigen space in a distributed manner, allowing it to efficiently scale without relying on a centralized controller. Our negative selection algorithm based on the immune system provides effective and scalable distributed anomaly detection for biosurveillance. It detects anomalies in the large, complex data from modern health monitoring data feeds with low false positive rates. Our bootstrap aggregation method improves performance on high-dimensional data sets, and we implement a parallelized version of the algorithm to demonstrate the potential to implement it on a scalable distributed architecture. Our negative selection algorithm is able to detect 90% of all outbreaks with a false positive rate of 11.8% in a publicly available multimodal synthetic health record data set. The scalability and performance of the negative selection algorithm demonstrate that immune computation can provide effective approaches for national and global scale biosurveillence.
KW - anomaly detection
KW - artificial immune system
KW - bootstrap aggregation
KW - negative selection
UR - http://www.scopus.com/inward/record.url?scp=85046084215&partnerID=8YFLogxK
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U2 - 10.1109/SSCI.2017.8285356
DO - 10.1109/SSCI.2017.8285356
M3 - Conference contribution
AN - SCOPUS:85046084215
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -