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There has been an increasing research interest in classification for data streams. Due to the evolving nature of data streams, it is a highly challenging issue to detect the appearance of concept drifts, which will make the current classification model invalid as time passes. So far most stream classification solutions exploit the so-called incremental learning process to continuously track the deviation of prediction accuracy. Unfortunately, to achieve the prompt concept-drifting detection, such strategies usually rely on an infeasible assumption about the availability of data instances with true labels. We in this paper propose a new framework, called Inference of Concept Evolution (abbreviated as ICE), to minimize the need of real-time acquisition of true labels. Specifically, the ICE framework is devised based on the idea of model reuse. The dictionary learning technique is utilized to determine whether the concept drift appears without the need of label acquisition. When the drift happens, the ICE framework will select the best model maintained in the model pool, decreasing the need of model re-training and its costly label acquisition. As demonstrated in our experimental result, the ICE framework can track the best model correctly and efficiently, showing its feasibility in real cases. Predicted results Current Classification ModelUnlabeled instances … Experts True results Conceptdrifting Detection Model Re-training ModuleUp-to-date classification model

BACKGROUND Healthcare workers (HCWs) are recommended to measure their body temperature every 8 hours to reduce the risk of cross infections during the COVID-19 pandemic in Taiwan. However, temperature reporting accuracy among HCWs is difficult to attain due to busy working schedules and high chances of human errors. OBJECTIVE This study describes the application of a continuous temperature monitoring system (HEARThermo Care AI.) based on the Internet of Things (IoT) among HCWs in hospitals during the COVID-19 outbreak. METHODS A prospective cohort study was conducted among HCWs in a major tertiary hospital in southern Taiwan. HCWs participated in this study wore HEARThermo, an innovative wearable device used to measure body surface temperature and heart rate every 10s, to continue monitoring their body surface temperature and heart rate during working hours. The HEARThermo Care AI. system combined with the routine body temperature measurement flow were used to automatedly notify the manager about the HCWs with fever risks. The completion rate of body temperature measurements was calculated as the number of HCWs using the continuous temperature monitoring system divided by the number of HCWs on duty. RESULTS A total of 52 HCWs (medical doctors, nurses, and interns) working in the medical ward between April 22 and June 30, 2020, voluntarily participated. The completion rate of body temperature measurements increased from 77.7% to 85% among HCWs in hospitals using HEARThermo Care AI. system. All the HCWs who received warning messages were reconfirmed by their managers and found they had discomforts at that time. CONCLUSIONS The application of the continuous temperature monitoring system serves as a solution to early identify HCWs suspected of having discomforts during the COVID-19 pandemic.