Consistent discovery of frequent interval-based temporal patterns in chronic patients’ data

Alexander Shknevsky, Yuval Shahar, Robert Moskovitch

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Increasingly, frequent temporal patterns discovered in longitudinal patient records are proposed as features for classification and prediction, and as means to cluster patient clinical trajectories. However, to justify that, we must demonstrate that most frequent temporal patterns are indeed consistently discoverable within the records of different patient subsets within similar patient populations. We have developed several measures for the consistency of the discovery of temporal patterns. We focus on time-interval relations patterns (TIRPs) that can be discovered within different subsets of the same patient population. We expect the discovered TIRPs (1) to be frequent in each subset, (2) preserve their “local” metrics - the absolute frequency of each pattern, measured by a Proportion Test, and (3) preserve their “global” characteristics - their overall distribution, measured by a Kolmogorov-Smirnov test. We also wanted to examine the effect on consistency, over a variety of settings, of varying the minimal frequency threshold for TIRP discovery, and of using a TIRP-filtering criterion that we previously introduced, the Semantic Adjacency Criterion (SAC). We applied our methodology to three medical domains (oncology, infectious hepatitis, and diabetes). We found that, within the minimal frequency ranges we had examined, 70–95% of the discovered TIRPs were consistently discoverable; 40–48% of them maintained their local frequency. TIRP global distribution similarity varied widely, from 0% to 65%. Increasing the threshold usually increased the percentage of TIRPs that were repeatedly discovered across different patient subsets within the same domain, and the probability of a similar TIRP distribution. Using the SAC principle, enhanced, for most minimal support levels, the percentage of repeating TIRPs, their local consistency and their global consistency. The effect of using the SAC was further strengthened as the minimal frequency threshold was raised.

Original languageEnglish
Pages (from-to)83-95
Number of pages13
JournalJournal of Biomedical Informatics
StatePublished - 1 Nov 2017


  • Classification
  • Clustering
  • Frequent pattern mining
  • Pattern consistency
  • Pattern repetition
  • Prediction
  • Temporal abstraction
  • Temporal data mining
  • Temporal knowledge discovery
  • Time intervals mining

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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