A Novel Method for Mining Abnormal Behaviors in Social Medical Insurance

Big Data-Driven Abnormal Behavior Detection in Healthcare Based on Association Rules

“…As in Reference [9], we apply the classic frequent item set mining algorithms Apriori [33], Eclat [34] and BP-Growth [35] to the frequent pattern mining in this paper and compare with this method. When min_row = 2, the running time of these methods under different data amounts is shown in Figure 7.…”

“…In this section, we propose a distributed fraud detection method based on the definition of medical insurance gathering behavior in reference [9], which can mine data records that may participate in aggregation fraud from medical insurance data. This problem is a novel and practical fraud detection problem.…”

“…According to the definition of the medical insurance gathering behavior in the reference [9], we can know that this behavior usually manifests as multiple medical insurance cards being consumed in the same place too frequently at the same time when the patient is in the hospital. This phenomenon of medical agglomeration may be a tendency to violate the rules: one person with multiple medical insurance cards may consume several cards for one treatment.…”

“…There is a special medical phenomenon in the analysis of medical insurance fraud. This special phenomenon is usually manifested in the fact that multiple medical insurance cards are consumed too frequently at the same time, which is called the medical agglomeration behaviors [9]. This kind of behaviors may be conducted by certain special illness groups, such as chronic patients, and may also be a kind of fraud.…”