AI-Driven Clinical Data Engineering for Real-Time Precision Medicine
Keywords:
Electronic Health Record (EHR) Analytics,Precision Medicine,AI-Driven Clinical Data Engineering,Deep Healthcare Architectures,Real-Time Healthcare Analytics,Clinical Decision Support Systems (CDSS),Healthcare Big Data Integration,Predictive Healthcare Modeling,Machine Learning in Healthcare,Intelligent Clinical Data Management.Abstract
Medicine is progressively evolving towards a personalized approach, and it soon may be possible to administer the right treatment to the right patient at the right time. Timely inference of mortality risk or the need for curative-intent therapy greatly influences decision-making in critical situations, such as in the intensive care unit and in the emergency department. Passive surveillance of patients who are constantly monitored and undergo frequent routine testing leverages this goal. Translational work encompasses clinical data engineering and the application of machine-learning algorithms to enable real-time systems that detect patient deterioration and support clinical decisions. The availability of clinical data stored in electronic health record systems has attracted great interest in automatic patient management, offering the potential for augmentation of clinical decisions or even automatic, risk-averse intervention.
Timely and reliable decision-support systems are developed and validated, allowing for rapid inference of patients’ risk for various outcomes during the course of a hospital admission. Carefully crafted closing metrics determine when a system must produce a reliable prediction while remaining in “wait and see” mode otherwise. Such metrics assess not only accuracy but also the validity of a signed prediction. Consistency analysis during this phase emphasizes expected clinical behavior: predictions should not flip or wander over time and most patients should not enter an active waiting mode.
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