Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

中文翻译：

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对电子健康记录的基于上下文的预测访问控制

由于电子辅助医疗保健服务需要患者的敏感健康记录，因此需要有效的访问控制技术。在危及患者健康的紧急情况下，应授予与紧急情况相关的不同医疗保健人员访问患者电子健康记录 (EHR) 的权限。我们研究的研究目标是开发基于患者时间顺序健康指标的机器学习技术，并将其与基于属性的访问控制 (ABAC) 机制相结合。我们提出了一种 ABAC 机制，可以通过应用预测上下文处理程序来访问敏感的 EHR 系统，其中上下文信息用于识别紧急情况并允许访问医疗记录。具体来说，我们通过利用长短期记忆 (LSTM) 神经网络 (NN) 使用患者最近的健康史来预测接下来两个小时的健康指标。这些预测的健康指标值由我们的个性化模糊上下文处理程序进行评估，以预测患者状态的严重性。开发的访问控制方法为急诊临床医生提供了对敏感信息的安全访问，同时保障了患者的健康。将这种预测机制与个性化上下文处理程序相结合被证明是一种强大的工具，可以提高现代 EHR 系统的访问控制机制的性能。预测患者状态的危急程度。开发的访问控制方法为急诊临床医生提供了对敏感信息的安全访问，同时保障了患者的健康。将这种预测机制与个性化上下文处理程序相结合被证明是一种强大的工具，可以提高现代 EHR 系统的访问控制机制的性能。预测患者状态的危急程度。开发的访问控制方法为急诊临床医生提供了对敏感信息的安全访问，同时保障了患者的健康。将这种预测机制与个性化上下文处理程序相结合被证明是一种强大的工具，可以提高现代 EHR 系统的访问控制机制的性能。