Study on the Intelligent Risk Warning and Control System in Hospitals

抄録

This study introduces a hybrid method to analyze the abnormal information in big data to precisely monitor, distinguish and exclude anomaly. First, statistical data anomalies are defined and strictly analyzed based on probability distribution events for the purpose of determination of anomaly analysis theory. Then, deep learning is used to monitor and distinguish abnormal data and link the statistical anomaly with practical risk management problems in the public healthcare. Last, three performance indicators from some public hospital are chosen for experiment to check the effectiveness of the proposed method in data anomaly monitoring, distinguishing and exclusion. The experiment result shows the availability of this method in data anomaly identification, processing and control.