Work

Temporal Rule Mining for Enhanced Risk Pattern Extraction: A Case Study with Acute Kidney Injury.

Year: 2025

Type: article

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.

Source: PubMed

Authors: Ho Yin Chan, Alan S Yu, Mei Liu

Institution: University of Florida

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Awards: Identifying Personalized Risk of Acute Kidney Injury with Machine Learning, Personalized Machine Learning for Acute Kidney Injury Prediction and Prognosis