Vincent-Paulraj, Alexandar, Burnside, Girvan 
ORCID: 0000-0001-7398-1346, Coenen, Frans ORCID: 0000-0003-1026-6649, Pirmohamed, Munir ORCID: 0000-0002-7534-7266 and Walker, Lauren ORCID: 0000-0002-3827-4387
(2021)
Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity.
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Abstract
Sequential rule mining is a well-established data mining technique for binary valued data. Many variations have been proposed, most approaches use the support-confidence-lift framework. Existing approaches make assumptions concerning the definition of what a sequence is. However, this definition is application dependent. In this paper we look at sequential rule mining with respect to multi-morbidity disease prediction which entails a rethink of the definition of what a sequence is, and a consequent rethink of the operation of the support-confidence-lift framework. A novel sequential rule mining algorithm is proposed designed to address the challenge of multi-morbidity disease prediction. The SEquential RElational N-DIsease Pattern (SERENDIP) algorithm.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Uncontrolled Keywords: | Sequential rule mining, Multi-morbidity disease prediction |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 12 Oct 2021 10:48 |
| Last Modified: | 27 Nov 2023 05:08 |
| DOI: | 10.1007/978-3-030-91100-3_20 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3140162 |
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