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1. 发明申请

US20130085769A1 CHARACTERIZING HEALTHCARE PROVIDER, CLAIM, BENEFICIARY AND HEALTHCARE MERCHANT NORMAL BEHAVIOR USING NON-PARAMETRIC STATISTICAL OUTLIER DETECTION SCORING TECHNIQUES 审中-公开
标题翻译：表征健康保险提供者，索赔，受益和健康商品正常行为使用非参数统计出口检测分选技术
公开(公告)号：US20130085769A1
公开(公告)日：2013-04-04
申请号：US13617085
申请日：2012-09-14
申请人： Allen Jost , Rudolph John Freese , Walter Allan Klindworth
发明人： Allen Jost , Rudolph John Freese , Walter Allan Klindworth
IPC分类号： G06Q50/22
CPC分类号： G06F19/328 , G06Q50/22 , G16H10/60
摘要： This invention uses non-parametric statistical measures and probability mathematical techniques to calculate deviations of variable values, on both the high and low side of a data distribution, from the midpoint of the data distribution. It transforms the data values and then combines all of the individual variable values into a single scalar value that is a “good-ness” score. This “good-ness” behavior score model characterizes “normal” or typical behavior, rather than predicting fraudulent, abusive, or “bad”, behavior. The “good” score is a measure of how likely it is that the subject's behavior characteristics are from a population representing a “good” or “normal” provider, claim, beneficiary or healthcare merchant behavior. The “good” score can replace or compliment a score model that predicts “bad” behavior in order to reduce false positive rates. The optimal risk management prevention program should include both a “good” behavior score model and a “bad” behavior score model.
摘要翻译：本发明使用非参数统计量度和概率数学技术从数据分布的中点计算数据分布的高低侧的变量值的偏差。它转换数据值，然后将所有单个变量值组合为一个良好评分的单个标量值。这种良性行为评分模型描绘了正常或典型的行为，而不是预测欺诈，辱骂或坏行为。良好的分数是衡量受试者的行为特征来自代表良好或正常提供者，索赔，受益人或医疗保健商人行为的人群的可能性。良好的分数可以取代或补充一个预测不良行为的分数模型，以减少假阳性率。最佳风险管理预防计划应包括良好行为评分模型和不良行为评分模型。

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