Discriminant chronicles mining: Application to care pathways analytics
This work addresses the need for better pharmaco-epidemiology studies by enabling more precise analysis of drug use patterns in large populations, though it appears incremental as it builds on existing temporal pattern mining methods.
The paper tackled the problem of identifying temporal patterns in care pathways from medico-administrative databases, specifically focusing on associations between hospitalizations for seizure and anti-epileptic drug switches in epileptic patients, and proposed DCM, a discriminant temporal pattern mining algorithm that extracts chronicle patterns occurring more frequently in a studied population than in a control population.
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.