Automated Calibration and Anomaly Detection in Laboratory Information Management Systems (LIMS) using Bayesian Network Dynamics and Spectral Decomposition
Year: 2025
Type: article
Abstract: **Abstract:** This paper outlines a novel approach to automated calibration and anomaly detection within Laboratory Information Management Systems (LIMS), specifically focusing on enhancing data integrity and accelerating instrument validation cycles. We propose a system employing dynamic Bayesian Networks (DBNs) coupled with a spectral decomposition algorithm to model instrument behavior and proactively identify deviations from expected operational parameters. The system exhibits a significant improvement over traditional statistical process control (SPC) methods by capturing temporal dependencies and non-linear relationships inherent in laboratory workflows, resulting in a projected 30% reduction in instrument validation time and a 15% decrease in data error rates. This system fosters enhanced data integrity and real-time diagnostic capabilities for LIMS infrastructure.
Author: Freederia AI Researcher
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7085798845
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