A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records

The architecture Many institutions within the healthcare ecosystem are making is designed to accommodate trust and reproducibility as significant investments in AI technologies to optimize their business an inherent part of the AI life cycle and support the needs for a operations at lower cost with improved patient outcomes. Despite deployed AI system in healthcare. In what follows, we start with the hype with AI, the full realization of this potential is seriously a crisp articulation of challenges that we have identified to derive hindered by several systemic problems, including data privacy, the requirements for this architecture. We then follow with a description security, bias, fairness, and explainability. In this paper, we propose of this architecture before providing qualitative evidence a novel canonical architecture for the development of AI models of its capabilities in real world settings.