Anomaly Detection of Clinical Behavior Sequences

Hebiao Yang, Xiaodong Yuan


The identification of abnormal clinical behavior during the process of treatments is of great significance for regulating the standard medical behavior. Due to clinical behavior constrained by time, and the timing of subsequence, GSP algorithm was modified in the present paper, and described the timing of subsequence by the introduction of the concept of legal subsequences in order to detect the frequent patterns in sequences; sequence association rules in accordance with the characteristics of territorial behavior were screened using association rule methods in order to establish rule base; Comparing the similarity between the detected frequent patterns and normal behavior rules, anomaly detection of the detected behavior was operated and the validity of the methods was verified through experiments.

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Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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