Patient privacy information desensitization method for medical text

Abstract

The invention provides a medical text-oriented patient privacy information desensitization method, which comprises the following steps of: extracting a patient medical text, and dividing patient privacy into three types according to all appearing patient privacy data, namely a digital type, a date type and a named entity type; designing a set of regular expressions for the digital privacy data, and removing digital information including patient ages, identity card numbers, contact information, postal codes, patient hospitalization numbers and the like; designing a random migration algorithm for the date type privacy data, identifying different types of date data, and migrating the date to a certain time of several future century; adopting a hidden Markov model for the named entity type privacy data, removing privacy information such as the patient name, the doctor name and the hospital name. According to the method, the personal sensitive information of the patient contained in the medical text is effectively removed, the privacy security of the patient in the medical data publishing process is guaranteed, and the method has important significance in medical data sharing and regional medical and health information big data platform construction.

Description

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AI Technical Summary

Benefits of technology

The technical effect of this patented technology described for improving patient privity during clinic visits involves providing a way to discretely identify patients with their private information without manually identifying them or requiring professional involvement.

Problems solved by technology

This patented technical problem addressed in this patents relates to protecting sensitive medical data from being leaked without losing important details about their content. Current approaches involve either deleting irrelevant parts (K) or applying special techniques like masked logins or identifying specific attributes instead of just specifying them all together. These solutions have limitations due to factors including human error, lack of knowledge base, time consumption required, etc., making these processes slow down. Therefore, current methods require significant effort and resources compared to more advanced technologies.

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