ABSTRACT
This research proposes a machine learning framework to predict and alert redundancy in multi-panel metabolic and coagulation tests, leveraging temporal patterns, inter-test correlations, and patient-specific clinical context.
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Key findings
Proposed a novel ML framework for predictive alerting of test redundancy.
Integrates temporal redundancy detection, cross-panel correlation, and personalized risk stratification.
Expected to reduce redundant testing by 20-30% without compromising patient safety.
Limitations & open questions
The approach requires comprehensive validation through retrospective analysis and prospective studies.
Implementation challenges in real-world clinical settings may affect the system's effectiveness.