Medical big data access control method and device and computer readable storage medium

An access control and big data technology, applied in the field of medical data privacy protection, which can solve problems such as difficult application, ignoring uncertainty and big data environment, etc.

Active Publication Date: 2021-01-22
YUNNAN UNIVERSITY OF FINANCE AND ECONOMICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, many scholars have provided different access control methods according to different policies, such as traditional access control (including autonomous access control, mandatory access control) and role-based access control methods. Static authorization method, using a fixed strategy, does not consider uncertainty and big data environment, ...

Method used

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  • Medical big data access control method and device and computer readable storage medium
  • Medical big data access control method and device and computer readable storage medium
  • Medical big data access control method and device and computer readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0181] S1. Preprocessing of historical visit data: Divide doctors into different departments, and code the historical medical records visited by each doctor in the same department within a period of time according to ICD-10 codes; In the corresponding disease code, it is marked with "1". If the code in the historical visit record of a doctor is not in the corresponding disease code of the department, it is marked with "0", and then the labels of all doctors in the same department are converted into one Boolean matrix, the different columns in the matrix represent the historical records of doctors' visits, and the rows represent the collection of medical records of a single doctor's visits in the same department;

[0182] S2. Construct doctor matrix:

[0183] The doctor's similarity matrix is ​​composed of the similarity of historical visit records between doctors, let s ij is the similarity between the i-th doctor and the j-th doctor, then the doctor similarity matrix S is ex...

Embodiment 2

[0210] S1. Preprocessing of historical visit data: Divide doctors into different departments, and code the historical medical records visited by each doctor in the same department within a period of time according to ICD-10 codes; In the corresponding disease code, it is marked with "1". If the code in the historical visit record of a doctor is not in the corresponding disease code of the department, it is marked with "0", and then the labels of all doctors in the same department are converted into one Boolean matrix, the different columns in the matrix represent the historical records of doctors' visits, and the rows represent the collection of medical records of a single doctor's visits in the same department; after obtaining the Boolean matrix, the same historical visits records of different doctors in the same department are merged to obtain the deduplication Matrix of historical access records;

[0211] S2. Construct doctor matrix:

[0212] The doctor's similarity matrix...

Embodiment 3

[0264] Embodiment 3: simulation test experiment

[0265] Obtain experimental data from a hospital, and the data types include text data, image data, and video data. According to the requirements of the experimental test of the method of the present invention, only part of the data is extracted from the data for experimentation;

[0266] Experimental setup: Divide doctors into two categories according to the method of the present invention, then simulate the visit requests of the two types of doctors, calculate and compare the average risk value of the two types of doctors, and observe whether the risk value of honest doctors is much lower than that of malicious doctors ;

[0267] In the simulation experiment, we divided doctors in the same department into honest doctors and malicious doctors according to their historical visit records, set doctors without visit history as honest doctors, and used Z, z i Represents the hierarchy of ICD-10 disease codes, where Z represents t...

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Abstract

The invention discloses a medical big data access control method and device and a computer readable storage medium. The medical big data access control method comprises the steps of historical accessdata preprocessing, doctor matrix construction, doctor clustering, risk quantification of medical record access and medical record access control. The device comprises a historical access data preprocessing module, a doctor matrix construction module, a doctor graph cutting module and a medical record access risk quantification and medical record access control module. Computer-readable instructions are stored on the computer-readable storage medium, and the method is implemented when the computer-readable instructions are executed. According to the method, historical access data of doctors are preprocessed, doctor matrixes are constructed, doctor graphs are cut, the doctors are clustered into two classes through spectral clustering, access record risk values requested by different classesof doctors are calculated, finally, judgment is conducted, and decisions are made for doctor access requests. The doctor access can be controlled accurately. The medical data leakage risk is reduced.

Description

technical field [0001] The invention belongs to the technical field of medical data privacy protection, and in particular relates to a medical big data access control method, device and computer-readable storage medium. Background technique [0002] With the rapid development of Internet information technology, all walks of life have entered the era of big data. Especially in the medical field, the era of big data has created an unprecedented great integration of digital, health, and medical care. This fusion brings great value and potential to new medical research and new health services. In the environment of medical and health big data, based on data standards and data integration, doctors can use historical information and even genetic information of patients in the process of diagnosis and treatment. In this way, doctors can provide specific patients with better effects and more targeted specific treatment plans at specific points in time, and medical and health big d...

Claims

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Application Information

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IPC IPC(8): G16H40/20G06Q10/06
CPCG06Q10/0635G16H40/20
Inventor 姜茸韩姗姗
Owner YUNNAN UNIVERSITY OF FINANCE AND ECONOMICS
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