Abstract
Technologies such as machine learning and artificial intelligence have brought about a tremendous change to biomedical computing and intelligence health care. As a principal component of the intelligence healthcare system, the hospital information system (HIS) has provided great convenience to hospitals and patients, but incidents of leaking private information of patients through HIS occasionally occur at times. Therefore, it is necessary to properly control excessive access behavior. To reduce the risk of patient privacy leakage when medical data are accessed, this article proposes a dynamic permission intelligent access control model that introduces credit line calculation. According to the target given by the doctor in HIS and the actual access record, the International Classification of Diseases (ICD)-10 code is used to describe the degree of correlation, and the rationality of the access is formally described by a mathematical formula. The concept of intelligence healthcare credit lines is redefined with relevance and time Windows. The access control policy matches the corresponding credit limit and credit interval according to the authorization rules to achieve the purpose of intelligent control. Finally, with the actual data provided by a Grade-III Level-A hospital in Kunming, the program code is written through machine learning and biomedical computing-related technologies to complete the experimental test. The experiment proves that the intelligent access control model based on credit computing proposed in this study can play a role in protecting the privacy of patients to a certain extent.
Highlights
Medical big data [1] is a branch of big data in the field of biomedicine
Given the medical privacy leakage risk arising from the widespread use of intelligent medical systems today, this study proposes an access control model based on credit line calculations for intelligent medical systems
This study introduces the concept of credit line in the intelligent medical system, and redefines it as a comprehensive evaluation of the history records, access behavior, and other factors of medical information system, and calculates and grants credit line of the doctor user for overdraft use
Summary
Medical big data [1] is a branch of big data in the field of biomedicine. It refers to the data related to life, health, and medical care generated in activities related to human health, mainly from intelligent medical systems such as clinical data, hospital, operation, biomedical research, disease prevention and control, health protection and food safety, public health and health management data, health care and other aspects [2]. Given the medical privacy leakage risk arising from the widespread use of intelligent medical systems today, this study proposes an access control model based on credit line calculations for intelligent medical systems. In this model, when doctors use the intelligent medical system to diagnose patients, they use historical records to calculate credit lines, and dynamically restrict doctors’ access rights based on their credit capabilities. 3. After comparison, more appropriate trust calculation and weight calculation methods are selected to achieve the effect of using historical records to restrain the behavior of doctors and reduce the risk of privacy disclosure in the medical field
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