School of information, Yunnan University of Finance and Economics, Kunming, China.
Key Laboratory of Service Computing and Safety Management of Yunnan Provincial Universities, Kunming, China.
Health Care Manag Sci. 2020 Sep;23(3):427-442. doi: 10.1007/s10729-019-09490-4. Epub 2019 Jul 23.
With the rapid development of modern information technology, the health care industry is entering a critical stage of intelligence. Faced with the growing health care big data, information security issues are becoming more and more prominent in the management of smart health care, especially the problem of patient privacy leakage is the most serious. Therefore, strengthening the information management of intelligent health care in the era of big data is an important part of the long-term sustainable development of hospitals. This paper first identified the key indicators affecting the privacy disclosure of big data in health management, and then established the risk access control model based on the fuzzy theory, which was used for the management of big data in intelligent medical treatment, and solves the problem of inaccurate experimental results due to the lack of real data when dealing with actual problems. Finally, the model is compared with the results calculated by the fuzzy tool set in Matlab. The results verify that the model is effective in assessing the current safety risks and predicting the range of different risk factors, and the prediction accuracy can reach more than 90%.
随着现代信息技术的飞速发展，医疗保健行业正在进入智能化的关键阶段。面对日益增长的医疗保健大数据，信息安全问题在智能医疗保健管理中越来越突出，特别是患者隐私泄露问题最为严重。因此，加强大数据时代智能医疗保健的信息管理是医院长期可持续发展的重要组成部分。本文首先确定了影响健康管理大数据隐私泄露的关键指标，然后基于模糊理论建立了风险访问控制模型，用于智能医疗大数据的管理，解决了在处理实际问题时由于缺乏真实数据而导致实验结果不准确的问题。最后，将模型与 Matlab 中模糊工具集计算的结果进行比较。结果验证了该模型在评估当前安全风险和预测不同风险因素范围方面的有效性，预测准确率可达 90%以上。
