PU learning-based medical equipment performance index anomaly detection method and device

Abstract

The invention provides a medical equipment performance index anomaly detection method and device based on PU learning, and the method comprises the steps: taking a historical key performance index (KPI) flow as training data, carrying out the clustering of the training data according to a similarity degree, obtaining a centroid curve of each cluster, and carrying out the marking of the centroid curve of each cluster, obtaining first abnormal annotation data and first non-annotation data; based on the first abnormal annotation data and the first unannotated data, constructing a binary classifier through positive example unannotated PU learning, and obtaining an abnormal label and a normal label of a centroid curve of each cluster in combination with active learning; and obtaining a label on the centroid curve of the cluster corresponding to the to-be-detected KPI flow, training an anomaly detection model corresponding to the to-be-detected KPI flow through semi-supervised learning, and detecting the to-be-detected KPI flow through the anomaly detection model. According to the method, the accuracy of medical equipment performance index anomaly detection is improved while the labeling workload is reduced to the maximum extent.

Description

technical field [0001] The present application relates to the technical field of data detection, in particular to a method and device for abnormal detection of performance indicators of medical equipment based on PU learning. Background technique [0002] At present, in order to ensure the reliability of medical equipment, professionals in the medical field need to continuously collect and monitor a large number of Key Performance Indicator (KPI) data streams of medical equipment, so that when medical equipment is abnormal, it can be repaired in time equipment, therefore, KPI anomaly detection is critical for medical equipment management. [0003] In related technologies, in the medical field, the KPI flow anomaly detection technology usually includes a supervised algorithm, a semi-supervised algorithm and an unsupervised algorithm. Among them, the supervised algorithm needs to manually label all the samples in the training set, and uses the characteristics and labels of th...

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Problems solved by technology

[0004] However, the applicant found that among the above algorithms, the supervised algorithm requires a large amount of labeled data, but in actual situations, KPI streams are often large-scale and diverse, and this labeling work requires a lot of time and effort. Difficult to implement; unsupervised algorithms have low precision and require a large amount of training data. In actual scenarios, the pattern of KPI flow often changes dynamically, so this algorithm has poor practical applicability; although semi-supervised algorithms are compared with The supervised algorithm reduces the cost of labeling and improves the detection accuracy of the model compared with the unsupervised algorithm. However, it still requires staff to accurately label all abnormalities in a certain period of time in a large number of KPI streams, and repeatedly confirm whether a certain KPI exists. Exceptions still bring a large workload

Therefore, the detection methods in related technologies, whether they are supervised learning methods, semi-supervised learning methods, or unsupervised learning methods, cannot achieve accurate detection of large-scale, diverse, and dynamically changing KPI flows with very little labeling work. abnormal detection

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