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Temporal Pattern Discovery for Trends and Transient Effects: Its Application to Patient Records

Temporal Pattern Discovery for Trends and Transient Effects: Its Application to Patient Records. Presenter : Hsin-Yi Huang Authors : G. Niklas Norén, Andrew Bate, Johan Hopstadius, Kristina Star, I. Ralph Edwards. Outline. Motivation Objective Methods Experiment Discussion Comments.

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Temporal Pattern Discovery for Trends and Transient Effects: Its Application to Patient Records

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  1. Temporal Pattern Discovery for Trends and Transient Effects: Its Application to Patient Records Presenter : Hsin-Yi Huang Authors : G. Niklas Norén, Andrew Bate, Johan Hopstadius, Kristina Star, I. Ralph Edwards 2008.KDD.9

  2. Outline • Motivation • Objective • Methods • Experiment • Discussion • Comments

  3. Motivation • Earlier work on pattern discovery in event history data focuses on the order of events rather than exact relative timing and trend . • It is based on the absolute frequency of recurrence rather than the unexpectedness. • It is be used to investigate variations in the overall time to an event rather than the detailed temporal association between two different events. • It is difficult to distinguish true temporal association in large-scale pattern discovery of event history data.

  4. Objective • The authors propose a temporal pattern discovery framework to solve above-mentioned issues.

  5. Methods : event histories : event history data

  6. Methods • Disproportionality analysis: • Shrinkage : • Measure of temporal association x :a specific index event of interest y :a specific focus event of interest :the number of index events x with a focus event y in time interval t :the number of index events in the reference set with a focus event y in time interval t : event histories : event history data :the number of index events x with an event history that covers time interval t :the total number of index events in the reference set with an event history that covers time interval t. u: the time period of interest v: the predefined control period

  7. Experiment • Dataset • UK IMS Disease Analyzer • over two million patients • more than 120 million individual prescriptions • Implementation • first prescriptions of different drugs in at least 13 months • index event: the first prescription for all drugs • the set of focus event includes any medical events (ICD-10) • contrasted the first month immediately after the index event to a six months long control period. six months -27 -21 1

  8. Experiment • Results Beneficial effects Adverse drug reactions Underlying disease Co-prescription patterns Periodic patterns

  9. Discussion • The temporal pattern discovery framework has three major strengths: • a graphical statistical approach to summarizing and visualizing event history data. • statistical shrinkage to reduce the risk of spurious associations • a crossover comparison of two different time periods in the same event histories. • Their future works include • the evaluation of this framework for use in other event history data sets. • the potential combination of chronograph-based pattern discovery with a method for sequential pattern discovery.

  10. Comments • Advantage • a novel idea for temporal pattern discovery • a visual analytical method to interpreting quickly • Drawback • … • Application • Temporal pattern discovery

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