ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
Temporal Rule Mining for Enhanced Risk Pattern Extraction: A Case Study with Acute Kidney Injury.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
Funding
Abstract
Association rule mining is a widely used data mining technique to uncover knowledge from large datasets. In healthcare, it can reveal meaningful patterns within electronic health records (EHR) that inform clinical decision-making and treatment strategies. However, many studies neglect the temporal aspects of EHR data, potentially overlooking patterns linked to specific time periods or sequence of clinical events. Recent advancements have introduced methods for mining temporal association rules, offering enhanced predictive and descriptive insights. We propose a multi-step framework that utilizes temporal pattern mining algorithm to extract actionable and temporal risk patterns for acute kidney injury (AKI) from EHR data. Our algorithm identified approximately 3,313 rules with 10 actionable features, characterized by low support and high confidence. These rules have a median support of 0.055 and a median confidence of 0.58. We highlight key rules, explore their potential clinical implications, and present a network-based view to provide actionable insights.
Identifiers
40502220PMC12150717What OpenQuestion holds
Registered trials
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