Details

Autor(en) / Beteiligte

• Zmezm, Hareth
• Luna, José María
• Almeda, Eduardo
• Ventura, Sebastián

Titel

Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder

Ist Teil von

• IEEE access, 2024, Vol.12, p.14580-14595

Ort / Verlag

Piscataway: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)

Erscheinungsjahr

2024

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Sequential pattern mining is a dynamic and thriving research field that aims to extract recurring sequences of events from complex datasets. Traditionally, focusing solely on the order of events often falls short of providing precise insights. Consequently, incorporating the temporal intervals between events has emerged as a vital necessity across various domains, e.g. medicine. Analyzing temporal event sequences within patients’ clinical histories, drug prescriptions, and monitoring alarms exemplifies this critical need. This paper presents innovative and efficient methodologies for mining frequent chronicles from temporal data. The mined graphs offer a significantly more expressive representation than mere event sequences, capturing intricate details of a series of events in a factual manner. The experimental stage includes a series of analyses of diverse databases with distinct characteristics. The proposed approaches were also applied to real-world data comprising information about subjects suffering from sleep disorders. Alluring frequent complete event graphs were obtained on patients who were under the effect of sleep medication.