Declarative Sequential Pattern Mining of Care Pathways

Abstract : 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.
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Communication dans un congrès
Conference on Artificial Intelligence in Medicine in Europe, Jun 2017, Vienna, Austria. 24, pp.1161 - 266, 2017, 16th Conference on Artificial Intelligence in Medicine. 〈http://aime17.aimedicine.info/〉. 〈10.1007/978-3-319-59758-4_29〉
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Contributeur : Yann Dauxais <>
Soumis le : mercredi 26 juillet 2017 - 11:01:34
Dernière modification le : jeudi 12 avril 2018 - 01:54:50

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Thomas Guyet, André Happe, Yann Dauxais. Declarative Sequential Pattern Mining of Care Pathways. Conference on Artificial Intelligence in Medicine in Europe, Jun 2017, Vienna, Austria. 24, pp.1161 - 266, 2017, 16th Conference on Artificial Intelligence in Medicine. 〈http://aime17.aimedicine.info/〉. 〈10.1007/978-3-319-59758-4_29〉. 〈hal-01569023〉

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