Abnormal case identification method and device based on artificial intelligence, and computer equipment

Abstract

The invention relates to big data, and provides an abnormal case identification method based on artificial intelligence which is applied to a platform server. The method comprises the steps: acquiringdiagnosis and treatment project data corresponding to the same disease, taking diagnosis and treatment time as a unit, and taking the diagnosis and treatment project data as words in a text to conduct text construction, wherein the constructed text comprises the diagnosis and treatment project data of different users of the same disease in the corresponding diagnosis and treatment time period; performing topic model construction on the text to obtain corresponding topic vector distribution, wherein each topic vector distribution is corresponding to one topic and comprises a plurality of wordidentifiers and corresponding word weights, and the topics have different user topic weights for different users; aligning the topic vector distribution corresponding to each text and performing clustering to obtain diagnosis and treatment item categories corresponding to each user at different time to form a diagnosis and treatment sequence corresponding to the user; and inputting the diagnosis and treatment sequence corresponding to each user at different diagnosis and treatment time into the process mining model to obtain a standard clinical path corresponding to the disease type so as to identify the abnormal case.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method, device, computer equipment and storage medium for identifying abnormal cases based on artificial intelligence. Background technique [0002] With the further advancement of the reform and development of the medical insurance system, medical insurance fraud has also intensified. There are various means of medical insurance fraud, including excessive medical treatment, prescription of drugs in violation of regulations, disintegration of hospitalization, false names, swiping medical insurance cards on behalf of others, and illegal reimbursement. Faced with the huge threat of insurance fraud, the current medical insurance system lacks effective countermeasures. [0003] Traditional abnormal case recognition is usually carried out by each medical institution through a local server using a self-defined algorithm. For example, the first medical institution uses th...

AI Technical Summary

Problems solved by technology

[0003] Traditional abnormal case recognition is usually carried out by each medical institution through a local server using a self-defined algorithm. For example, the first medical institution uses the first recognition algorithm to identify abnormal cases through the first hospital server, and the recognition range of various self-defined algorithms Low, poor recognition accuracy, it is difficult to efficiently cover suspicious cases, and each medical institution needs to deploy recognition algorithms on the local server, wasting a lot of computer resources

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