Abstract
Knowledge discovery within the health informatics domain provides healthcare professionals with the ability to further inform their decision-making regarding patient treatment. Discovering patterns within this domain has however proven difficult for data scientists; one of the main challenges of knowledge extraction is the ability to extract meaningful patterns, which 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 pattern mining with fuzzy time-intervals.
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Mills-Mullett, A., Lu, J. (2015). Mining Fuzzy Time-Interval Patterns in Clinical Databases. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_33
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DOI: https://doi.org/10.1007/978-3-319-25032-8_33
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Online ISBN: 978-3-319-25032-8
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