Unsupervised Anomaly Detection in Real-Time FHIR Healthcare Streams
Keywords:
Adaptive Control ,Autonomous Vehicle , Dynamic Environment , Model-Free Reinforcement Learning , Safety , Traffic Efficiency.Abstract
Reports of serious security breaches involving medical data have attracted increasing public attention. These incidents emphasize the importance of protecting health information and re-establishing patient trust in information systems that support healthcare delivery. Guaranteeing the confidentiality and integrity of healthcare data is challenging, given the intrinsic requirements dictated by openness, interoperability, accessibility, and availability. In addition to the overall emergence of disorder, active, unattended FHIR data streams produced by thousands of devices, systems, and services are particularly prone to contain high-level attributes and momentary deviations that fall outside accepted monitoring thresholds or general patterns. Such anomalies may indicate dangerous problems, progress toward disastrous events, or even the introduction of malware. However, it is hardly possible to use supervised techniques to define the rich set of expected patterns that characterize or govern real-time healthcare data streams.
Unsupervised learning methods are presented with an objective, evidence-based analysis and formal structure. Emphasis is placed on the application of these approaches to real-time anomaly detection within unattended and open healthcare data streams that follow the Fast Healthcare Interoperability Resources (FHIR) standard. The analysis offers a heuristic indication of the methods that can best exploit the availability and characteristics of real-time healthcare data streams. The methods are systematically examined against qualifying criteria and success factors for their application in real-time healthcare environments.
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