skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Adverse Event Prediction Using Graph-Augmented Temporal Analysis: Final Report.

Abstract

This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to de- velop a method for analyzing multiple time-series data streams to identify precursors provid- ing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub- missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.

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:
1530166
Report Number(s):
SAND2018-11123
669598
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Brost, Randolph, Carrier, Erin E., Carroll, Michelle, Groth, Katrina M., Kegelmeyer, W. Philip, Leung, Vitus J., Link, Hamilton E., Patterson, Andrew John, Phillips, Cynthia A., Richter, Samuel, Robinson, David G., Staid, Andrea, and Woodbridge, Diane M.-K. Adverse Event Prediction Using Graph-Augmented Temporal Analysis: Final Report.. United States: N. p., 2018. Web. doi:10.2172/1530166.
Brost, Randolph, Carrier, Erin E., Carroll, Michelle, Groth, Katrina M., Kegelmeyer, W. Philip, Leung, Vitus J., Link, Hamilton E., Patterson, Andrew John, Phillips, Cynthia A., Richter, Samuel, Robinson, David G., Staid, Andrea, & Woodbridge, Diane M.-K. Adverse Event Prediction Using Graph-Augmented Temporal Analysis: Final Report.. United States. doi:10.2172/1530166.
Brost, Randolph, Carrier, Erin E., Carroll, Michelle, Groth, Katrina M., Kegelmeyer, W. Philip, Leung, Vitus J., Link, Hamilton E., Patterson, Andrew John, Phillips, Cynthia A., Richter, Samuel, Robinson, David G., Staid, Andrea, and Woodbridge, Diane M.-K. Mon . "Adverse Event Prediction Using Graph-Augmented Temporal Analysis: Final Report.". United States. doi:10.2172/1530166. https://www.osti.gov/servlets/purl/1530166.
@article{osti_1530166,
title = {Adverse Event Prediction Using Graph-Augmented Temporal Analysis: Final Report.},
author = {Brost, Randolph and Carrier, Erin E. and Carroll, Michelle and Groth, Katrina M. and Kegelmeyer, W. Philip and Leung, Vitus J. and Link, Hamilton E. and Patterson, Andrew John and Phillips, Cynthia A. and Richter, Samuel and Robinson, David G. and Staid, Andrea and Woodbridge, Diane M.-K.},
abstractNote = {This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to de- velop a method for analyzing multiple time-series data streams to identify precursors provid- ing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub- missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.},
doi = {10.2172/1530166},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {10}
}