2017/09/11 by Dauxais, Yann, Thomas Guyet, Guyet, Thomas +3
Computer Science · #Artificial Intelligence (cs.AI) #Data Mining Algorithms and Applications #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning in Healthcare #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.1709.03309
openalex publication_date 2017/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.