An in situ Bi reduction strategy to induce a preferential orientation that significantly enhanced the photocatalytic nitrogen fixation performance of Bi2WO6.
Healthcare insurance frauds are causing millions of dollars of public healthcare fund losses around the world in various ways, which makes it very important to strengthen the management of medical insurance in order to guarantee the steady operation of medical insurance funds. Healthcare fraud detection methods can reduce the losses of healthcare insurance funds and improve medical quality. Existing fraud detection studies mostly focus on finding normal behavior patterns and treat those violating normal behavior patterns as fraudsters. However, fraudsters can often disguise themselves with some normal behaviors, such as some consistent behaviors when they seek medical treatments. To address these issues, we combined a MapReduce distributed computing model and association rule mining to propose a medical cluster behavior detection algorithm based on frequent pattern mining. It can detect certain consistent behaviors of patients in medical treatment activities. By analyzing 1.5 million medical claim records, we have verified the effectiveness of the method. Experiments show that this method has better performance than several benchmark methods.INDEX TERMS Big data, abnormal behavior, healthcare insurance, association rules.
Big data have shown their great potential value to serve many aspects of human life. Due to complexity of the medical and healthcare big data in real life, traditional big data analysis methods are difficult to be dealt with. Therefore, a single method is unable to analyze and manage heterogeneous big data sources. To utilize data fully from the perspective of decision-making, we propose a novel framework which guides the healthcare big data to be smartly and proactively processed for decision-making without user interventions. The framework contains five stages, which are intelligent data cleaning, customized data fusion, analysis mapping, exploratory visualization analysis, and generation of decision-making reports. It also enables learning from the data and correlating them with the existing human knowledge. Subsequently, a smart big data-driven application exhibits innovative management in intelligent healthcare. The proposed framework provides the guidelines of the best practices of big data-driven analysis for intelligent healthcare according to our practical applications. The platform provides the appropriate reference for the big data-driven innovation of management in intelligent healthcare.
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