首页> 外国专利> RELEVANCE FEEDBACK TO IMPROVE THE PERFORMANCE OF CLUSTERING MODEL THAT CLUSTERS PATIENTS WITH SIMILAR PROFILES TOGETHER

RELEVANCE FEEDBACK TO IMPROVE THE PERFORMANCE OF CLUSTERING MODEL THAT CLUSTERS PATIENTS WITH SIMILAR PROFILES TOGETHER

机译:相关反馈可改善聚类模型的性能,聚类模型将具有相似特征的患者聚在一起

摘要

In patient cohort identification, clustering (30) of patients is performed using a patient comparison metric dependent on a set of features (24). Information is displayed on sample patients who are similar or dissimilar to a query patient according to the clustering. User inputted comparison values are received comparing the sample patients with the query patient. The set of features and/or feature weights are adjusted to generate an adjusted patient comparison metric having improved agreement with the user inputted comparison values. The clustering is repeated using the adjusted patient comparison metric. A patient cohort is identified from a cluster (34) containing the query patient produced by the last clustering repetition. The information on the sample patients may be shown by simultaneously displaying two or more graphical modality representations (70, 72, 74) each plotting the sample patients and the query patient against two or more features of the modality.
机译:在患者队列识别中,使用取决于一组特征(24)的患者比较度量来对患者进行聚类(30)。根据聚类,信息显示在与查询患者相似或不相似的样本患者上。接收用户输入的比较值,将样本患者与查询患者进行比较。调整该组特征和/或特征权重以生成与用户输入的比较值具有更好的一致性的调整后的患者比较度量。使用调整后的患者比较指标重复进行聚类。从包含由最后的聚类重复产生的查询患者的聚类(34)中识别患者队列。可以通过同时显示两个或更多个图形模态表示(70、72、74)来显示关于样本患者的信息,每个图形模态表示(70、72、74)分别针对模态的两个或更多个特征绘制样本患者和查询患者。

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