Impact of body roundness index on cognitive decline and cognitive impairment in middle-aged and older adults ≥ 45 years: mediating role of biological aging.

The terms “security” or “protection” in healthcare can often seem unclear or confusing because they mean different things in different situations. In simple terms, security in healthcare facilities refers to a system of measures designed to protect the property and ensure the safety of everyone who works in or visits the facility. However, what counts as “safe” is not fixed–something safe today might not be safe tomorrow. It’s hard to measure safety because it depends on changing circumstances and personal judgment. The main aim of security is not to completely eliminate all risks but to reduce the chances of harmful incidents and limit the damage if they occur. The difficulty lies in detecting the abnormal activities, like unauthorized access to private information, because healthcare data systems are dynamic and distributed. Because insider attacks entail the unusual use of valid credentials, they frequently get past traditional security measures. Therefore, sophisticated analytics that can extract intricate behavioral patterns from access records are necessary to identify such subtle anomalies. Recent developments in deep learning (DL) and machine learning (ML) have created new opportunities for intelligent security monitoring. While supervised machine learning techniques like Random Forests and Support Vector Machines (SVM) have been used for intrusion detection, these models mostly rely on labeled datasets, which are hard to come by or unavailable in the healthcare industry because of privacy regulations. Unsupervised techniques have shown potential in detecting abnormalities without the need for explicit labels, especially deep generative models. The ability to learn compressed latent representations of normal data distributions and to reconstruct input patterns with minimal loss makes Variational Autoencoders (VAEs) unique among them. In EHR systems, VAEs are a good option for simulating typical user access behaviors since deviations in reconstruction error are useful signs of anomalies. Despite these advancements, there are still a number of gaps in the use of deep generative models for the security of healthcare data. Most recent research focuses on network intrusion detection rather than user behavior modeling in EHR environments. Furthermore, creating strong models that can generalize across real-world settings is made difficult by privacy issues and restricted data availability. Both methodological innovation and synthetic data augmentation techniques that replicate real EHR access patterns while maintaining confidentiality are needed to address these issues. A VAE-based anomaly detection framework is proposed in this study to detect anomalous access behaviors in healthcare data systems. Using realistic elements, including user roles, departments, timestamps, access frequency, and data sensitivity levels, a synthetically enhanced EHR access log dataset was produced in order to address the issue of data scarcity. The proposed model detects deviations that may indicate potential insider threats or data breaches by analyzing the latent space representation of common access behaviors. Because the VAE architecture may independently capture nonlinear behavioral dependencies, it is flexible and privacy-preserving compared to traditional rule-based or threshold-driven systems. This research is innovative because it develops a scalable and privacy-aware anomaly detection system for the medical industry by combining data augmentation techniques with unsupervised generative modeling. By providing an interpretable probabilistic mechanism for anomaly scoring, VAEs lower false-positive rates in this scenario compared to more conventional unsupervised techniques like Isolation Forest and One-Class SVM. Additionally, visualization elements that bridge the gap between deep learning interpretability and cybersecurity decision-making, including reconstruction error heatmaps and latent space clustering, make it simple for security analysts to spot odd patterns. The top-down structure of this study is shown in Fig. . VAE-based anomaly detection framework for secure EHR access.