Negative selection based anomaly detector for multimodal health data

Drew Levin, Melanie Moses, Tatiana Flanagan, Stephanie Forrest, Patrick Finley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-7
Number of pages7
Volume2018-January
ISBN (Electronic)9781538627259
DOIs
StatePublished - Feb 2 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: Nov 27 2017Dec 1 2017

Other

Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period11/27/1712/1/17

Keywords

  • anomaly detection
  • artificial immune system
  • bootstrap aggregation
  • negative selection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

Fingerprint Dive into the research topics of 'Negative selection based anomaly detector for multimodal health data'. Together they form a unique fingerprint.

Cite this