Declarative Sequential Pattern Mining of Care Pathways
This addresses the need for clinicians to express complex queries to generate fewer but more significant patterns in healthcare data analysis, representing an incremental improvement in domain-specific tools.
The paper tackled the problem of sequential pattern mining algorithms generating too many redundant patterns in care pathway databases by proposing a declarative approach based on Answer Set Programming, which was applied to a pharmaco-epidemiological study on seizures and antiepileptic drug switches.
Sequential pattern mining algorithms are widely used to explore care pathways database, but they generate a deluge of patterns, mostly redundant or useless. Clinicians need tools to express complex mining queries in order to generate less but more significant patterns. These algorithms are not versatile enough to answer complex clinician queries. This article proposes to apply a declarative pattern mining approach based on Answer Set Programming paradigm. It is exemplified by a pharmaco-epidemiological study investigating the possible association between hospitalization for seizure and antiepileptic drug switch from a french medico-administrative database.