Online early warning method for big data abnormality in medical cloud platform based on statistical generative model

Abstract

The invention discloses an online warning method for large data abnormality of a medical cloud platform based on a statistical generation model. The invention uses a two-step filtering method, including affine transformation and directional smoothing filtering, to filter time-series segment data, thereby removing time-series segment data The similarity points in , retain a small number of feature points, thereby reducing the amount of analysis data, and at the same time provide a data basis for the statistical generation model. For the search of abnormal early warning samples, this method adopts an online mixed Gaussian statistical generation model, which fits the probability distribution of the whole life cycle of medical data, can calculate the occurrence probability of real-time time series samples, and select the low probability sequence As an early warning sample, realize online early warning of abnormal big data on the medical cloud platform.

Description

technical field [0001] The invention relates to a big data abnormality judgment and early warning method of a medical cloud platform, in particular to a big data abnormality judgment and early warning method of a medical cloud platform based on a statistical generation model. Background technique [0002] A large amount of drug manufacturing, storage and circulation data, as well as data on patients' medication habits and methods are stored in the medical cloud platform. These data can often reflect the temporal and spatial distribution characteristics and future development trends of various drugs and related diseases. Industry workers may Concerned about the changes in the spatio-temporal distribution of a certain type of drug or a brand of drugs, or looking for potential causal relationships among all changes. In the face of massive big data, relying on regular reports in the past cannot meet the needs of the industry in terms of timeliness and operability, so it needs to...

AI Technical Summary

Problems solved by technology

Due to the relatively smooth changes in data such as manufacturing, logistics, and regional circulation in the medical field, the feature points extracted by the current method are still too dense, and a large number of similar and repeated features are retained, so that feature extraction cannot improve the efficiency of algorithm execution; The method of dynamic time window or clustering depends on the rationality of the definition of distance measurement for the sample sequence. For the data of medical cloud platform, there is no ideal distance measurement method at present.

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