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

Explore a novel temporal pattern discovery framework for analyzing patient records, identifying trends, transient effects, beneficial outcomes, and adverse reactions. Utilizing statistical methods and visual aids, this approach contrasts event histories for insightful analysis.

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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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