Abstract
Pharmaco-epidemiology (PE) is the study of uses and effects of drugs in well defined populations. As medico-administrative databases cover a large part of the population, they have become very interesting to carry PE studies. Such databases provide longitudinal care pathways in real condition containing timestamped care events, especially drug deliveries. Temporal pattern mining becomes a strategic choice to gain valuable insights about drug uses. In this paper we propose DCM, a new discriminant temporal pattern mining algorithm. It extracts chronicle patterns that occur more in a studied population than in a control population. We present results on the identification of possible associations between hospitalizations for seizure and anti-epileptic drug switches in care pathway of epileptic patients.
This research is supported by the PEPS project funded by the French National Agency for Medicines and Health Products Safety (ANSM).
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Notes
- 1.
\(\mathcal {T}_{\infty }\) is the set of temporal constraints with all bounds set to \(\infty \).
- 2.
ICD-10: International Classification of Diseases 10th Revision.
- 3.
This number of patients have been initially estimated important by epidemiologists to define a population of patients with similare care sequences associated to seizure.
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Dauxais, Y., Guyet, T., Gross-Amblard, D., Happe, A. (2017). Discriminant Chronicles Mining. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_26
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