tSPM+; a high-performance algorithm for mining transitive sequential
  patterns from clinical data
Jonas Hügel Ulrich Sax Shawn N. Murphy Hossein Estiri
The increasing availability of large clinical datasets collected from
patients can enable new avenues for computational characterization of complex
diseases using different analytic algorithms. One of the promising new methods
for extracting knowledge from large clinical datasets involves temporal pattern
mining integrated with machine learning workflows. However, mining these
temporal patterns is a computational intensive task and has memory
repercussions. Current algorithms, such as the temporal sequence pattern mining
(tSPM) algorithm, are already providing promising outcomes, but still leave
room for optimization. In this paper, we present the tSPM+ algorithm, a
high-performance implementation of the tSPM algorithm, which adds a new
dimension by adding the duration to the temporal patterns. We show that the
tSPM+ algorithm provides a speed up to factor 980 and a up to 48 fold
improvement in memory consumption. Moreover, we present a docker container with
an R-package, We also provide vignettes for an easy integration into already
existing machine learning workflows and use the mined temporal sequences to
identify Post COVID-19 patients and their symptoms according to the WHO
definition.