Temporal pattern discovery for anomaly detection in a smart home

2007 ◽  
Author(s):  
V. Jakkula ◽  
D.J. Cook ◽  
A.S. Crandall
Keyword(s):  
Anomaly Detection ◽  
Temporal Pattern ◽  
Smart Home ◽  
Pattern Discovery ◽  
Temporal Pattern Discovery

Enhancing Anomaly Detection Using Temporal Pattern Discovery

2009 ◽  
pp. 175-194 ◽  
Author(s):  
Vikramaditya R. Jakkula ◽  
Aaron S. Crandall ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Temporal Pattern ◽  
Pattern Discovery ◽  
Temporal Pattern Discovery

Empirical Performance of the Calibrated Self-Controlled Cohort Analysis Within Temporal Pattern Discovery: Lessons for Developing a Risk Identification and Analysis System

Drug Safety ◽  
2013 ◽  
Vol 36 (S1) ◽  
pp. 107-121 ◽  
Author(s):  
G. Niklas Norén ◽  
Tomas Bergvall ◽  
Patrick B. Ryan ◽  
Kristina Juhlin ◽  
Martijn J. Schuemie ◽  
...  
Keyword(s):  
Temporal Pattern ◽  
Cohort Analysis ◽  
Pattern Discovery ◽  
Risk Identification ◽  
Empirical Performance ◽  
Analysis System ◽  
Temporal Pattern Discovery

scholarly journals Cascading Spatio-Temporal Pattern Discovery

2012 ◽  
Vol 24 (11) ◽  
pp. 1977-1992 ◽  
Author(s):  
Pradeep Mohan ◽  
Shashi Shekhar ◽  
James A. Shine ◽  
James P. Rogers
Keyword(s):  
Temporal Pattern ◽  
Pattern Discovery ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery

scholarly journals Evaluating the performance of Temporal Pattern Discovery: New application using Statins and Rhabdomyolysis in OMOP databases

2020 ◽  
Author(s):  
M Lavallee ◽  
T Yu ◽  
L Evans ◽  
Mieke Van Hemelrijck ◽  
C Bosco ◽  
...  
Keyword(s):  
Signal Detection ◽  
Temporal Pattern ◽  
Pattern Discovery ◽  
Control Period ◽  
Data Sources ◽  
Health Improvement ◽  
Initial Exposure ◽  
Electronic Healthcare Databases ◽  
Useful Signal ◽  
Temporal Pattern Discovery

Abstract Background: Temporal Pattern Discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources. Methods: We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance. Results: Similar to previous findings, we noted an increase in the information component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1-30 days as compared to the control period of -180 to -1days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs. Conclusion: Our OMOP replication matched the results of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

Evaluating the performance of Temporal Pattern Discovery: New application using Statins and Rhabdomyolysis in OMOP databases

2019 ◽  
Author(s):  
M Lavallee ◽  
T Yu ◽  
L Evans ◽  
Mieke Van Hemelrijck ◽  
C Bosco ◽  
...  
Keyword(s):  
Signal Detection ◽  
Temporal Pattern ◽  
Pattern Discovery ◽  
Control Period ◽  
Risk Identification ◽  
Health Improvement ◽  
Initial Exposure ◽  
Electronic Healthcare Databases ◽  
Useful Signal ◽  
Temporal Pattern Discovery

Abstract Introduction Temporal Pattern Discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources.Methods We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance.Results Similar to previous findings, we noted an increase in the information component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1-30 days as compared to the control period of -180 to -1days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs.Conclusion Our OMOP replication matched the results of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare ADRs, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

Spatio-temporal Pattern discovery in sensor data: A multivalued decision systems approach

2016 ◽  
Vol 109 ◽  
pp. 137-146 ◽  
Author(s):  
Sanchita Mal-Sarkar ◽  
Iftikhar U. Sikder ◽  
Vijay K. Konangi
Keyword(s):  
Temporal Pattern ◽  
Systems Approach ◽  
Pattern Discovery ◽  
Sensor Data ◽  
Decision Systems ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery

scholarly journals Cascading Spatio-temporal Pattern Discovery: A Summary of Results

2010 ◽  
Author(s):  
Pradeep Mohan ◽  
Shashi Shekhar ◽  
James A. Shine ◽  
James P. Rogers
Keyword(s):  
Temporal Pattern ◽  
Pattern Discovery ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery
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