Mining frequent arrangements and sequencing of events for diseases prognosis using reference event-based temporal relations

Abstract

Mining temporal patterns has been an active research area in AI. Mining patterns of temporally correlated events called arrangements is required in many scientific domains, especially in the medical domain. Key events of the domain defined as reference events are used in describing the arrangements. In this work, mining events in arrangements MENSA algorithm is proposed to mine frequent arrangements and to generate sequential rules that can be used in early forecasting of probable diseases and symptoms and hence facilitate decision support related tasks. The MENSA algorithm has excellent scale-up property with respect to the size of the sequences. Temporal relations between events are defined using the contextual reference event-based temporal REseT relations. These relations can provide more useful knowledge about the order of events within an arrangement in comparison with Allen's relations and the effectiveness, applicability of these relations in prognosis is demonstrated using PREDICT algorithm with Percent_Similarity as the performance measure. The performance of the proposed algorithms is evaluated and they outperform other existing approaches when experimented on real finance dataset and synthetic medical dataset.