Mining meaningful patterns from numerical healthcare data is challenging, as continuous lab values are difficult to analyze directly and traditional association rule mining often generates arbitrary thresholds. We introduce Threshold-Aware Association Rules (TAAR), a framework that converts continuous lab values into semantic intervals and extracts interpretable rules using an enhanced Apriori algorithm. Large Language Models (LLMs) are employed to refine support and confidence thresholds, filter implausible rules, and produce natural-language explanations. Applied to blood test data, TAAR improves clinical usability, guides actionable follow-up recommendations, and supports informed decision-making.
Abboura et al. (Mon,) studied this question.