Surprising and novel multivariate sequential patterns using odds ratio for temporal evolution in healthcare

Isidoro J. Casanova, Manuel Campos, Jose M. Juarez, Antonio Gomariz, Bernardo Cánovas-Segura, Marta Lorente-Ros, Jose A. Lorente

Abstract: Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of interest for patterns are focused on reducing the number of patterns and quantifying their relevance (utility/usefulness). However, although the temporal dimension plays a key role in medical records, few efforts have been made to extract temporal knowledge about the patient’s evolution from multivariate sequential patterns.

External IDs:dblp:journals/midm/CasanovaCJGCLL24