Anomaly Detection Using Data Rate of Change on Medical Data

The identification and mitigation of anomaly data, characterized by deviations from normal patterns or singularities, stand as critical endeavors in modern technological landscapes, spanning domains such as Non-Fungible Tokens (NFTs), cyber-security, and the burgeoning metaverse. This paper presents a novel proposal aimed at refining anomaly detection methodologies, with a particular focus on continuous data streams. The essence of the proposed approach lies in analyzing the rate of change within such data streams, leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy. Through empirical evaluation, our method demonstrates a marked improvement over existing techniques, showcasing more nuanced and sophisticated result values. Moreover, we envision a trajectory of continuous research and development, wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios, ensuring adaptability and robustness in real-world applications.