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In healthcare, clinical prediction methodologies are essential for forecasting various patient health outcomes, including survival probabilities and expected hospitalization duration. These predictive models, as highlighted in the literature [1], [2], [3], are carefully crafted to provide healthcare professionals with improved decision-making support, especially in critical scenarios.
With the rapid expansion and advancement of industrial production, plant-wide industrial processes (PIPs) have gained increasing prominence and importance in modern industry [1]. In PIPs, a large number of sensors continuously collect vast amounts of operational data. Detecting anomalies in the monitoring data is essential for assessing the real-time operating conditions of the system, which is critical for ensuring safety and preventing economic losses.
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