Mining fuzzy time-interval patterns in clinical databases

Alex Mills-Mullett, Jing Lu

Research output: Chapter in Book/Report/Published conference proceedingConference contribution

Abstract

Knowledge discovery within the health informatics domain provides healthcare professionals with the ability to further inform their decisions regarding patient treatment. However discovering patterns within this domain has proven difficult for data scientists. One of the main challenges is finding meaningful patterns that can be used as effective support alongside specialist knowledge. This paper demonstrates the value behind extracting temporal patterns from live datasets. In particular it shows how fuzzy logic and divisive hierarchical clustering can be used to extract frequent sequential patterns with time-intervals from a series of breast cancer diagnoses. A discussion regarding the link between time-intervals and cancer treatment is explored through the use of multi-dimensional sequential patterns mining with fuzzy time-intervals.
Original languageEnglish
Title of host publicationThirty-fifth SGAI International Conference on Artificial Intelligence, 15-17 December 2015, Cambridge.
Pages399-404
Number of pages6
Publication statusPublished - 1 Dec 2015
Externally publishedYes

Fingerprint Dive into the research topics of 'Mining fuzzy time-interval patterns in clinical databases'. Together they form a unique fingerprint.

Cite this