scholarly journals Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery

2020 ◽  
Author(s):  
Kelly Grassi ◽  
Émilie Poisson-Caillault ◽  
André Bigand ◽  
Alain Lefebvre
Keyword(s):  
Time Series ◽  
Comparative Study ◽  
Hierarchical Clustering ◽  
Spectral Clustering ◽  
Temporal Pattern ◽  
Pattern Discovery ◽  
Unsupervised Segmentation ◽  
Pattern Segmentation ◽  
The Right ◽  
Temporal Pattern Discovery

Many clustering approaches succeed in pattern segmentation in many applications. This unsupervised segmentation should be effective to reduce an expert labelling time: i.e, they must be able to detect the number of patterns and identify them in a sequence or map with the right cuts. Several direct and hierarchical clustering approaches are compared for this task. A divisive spectral clustering architecture with a no-cut criteria is also proposed. This new algorithm achieves promise segmentation of spatial UCI databases and marine time series compared to other approaches.

A Temporal Pattern Discovery Method and its Application to Physiological Data: A Case Study (Preprint)

2019 ◽  
Author(s):  
Catherine Inibhunu ◽  
Carolyn McGregor
Keyword(s):  
Time Series ◽  
Temporal Pattern ◽  
Clinical Decision Making ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Mathematical Formulation ◽  
Series Data ◽  
Physiological Data ◽  
Time Period ◽  
Temporal Pattern Discovery

BACKGROUND High frequency data collected from monitors and sensors that provide measures relating to patients’ vital status in intensive care units (NICUs) has the potential to provide valuable insights which can be crucial when making critical decisions for the care of premature and ill term infants. However, this exercise is not trivial when faced with huge volumes of data that are captured every second at the bedside/home. The ability to collect, analyze and understand any hidden relationships in the data that may be vital for clinical decision making is a central challenge. OBJECTIVE The main goal of this research is to develop a method to detect and represent relationships that may exist in temporal abstractions (TA) and temporal patterns (TP) derived from time oriented data. The premise of this research is that in clinical care, the discovery of unknown relationships among physiological time oriented data can lead to detection of onset of conditions, aid in classifying abnormal or normal behaviors or derive patterns of an altered trajectory towards a problematic future state for a patient. That is, there is great potential to use this approach to uncover previously unknown pathophysiologies that are present in high speed physiological data. METHODS This research introduces a TPR process and an associated TPRMine algorithm which adopts a stepwise approach to temporal pattern discovery by first applying a scaled mathematical formulation of the time series data. This is achieved by modelling the problem space as a finite state machine representation where for a given timeframe, a time series data segment transitions from one state to another based on probabilistic weights and then quantifying the many paths a time series data may transition to. RESULTS The TPRMine Algorithm has been designed, implemented and applied to patient physiological data streams captured from the McMaster Children’s Hospital NICU. The algorithm has been applied to understand the number of states a patient in a NICU bed can transition to in a given time period and a demonstration of formulation of hypothesis tests. In addition, a quantification of these states is completed leading to creation of a vital scoring. With this, it’s possible to understand the percent of time a patient remains in a high or low vital score. CONCLUSIONS The developed method allows understanding the number of states a patient may transition to in any given time period. Adding some clinical context to the identified states facilitates state quantification allowing formulation of thresholds which leads to generating patient scores. This is an approach that can be utilized for identifying patient at risk of some clinical condition prior to disease progress. Additionally the developed method facilitates identification of frequent patterns that could be associated with generated thresholds.

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

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

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.

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