A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records

TL;DR

This paper introduces a comprehensive architecture for developing and managing AI predictive models in healthcare, aiming to address key challenges like privacy, bias, and explainability throughout the model lifecycle.

Contribution

It proposes a novel canonical architecture that systematically handles data ingestion, model development, and deployment, improving AI integration in healthcare.

Findings

Qualitative evaluation demonstrates effective handling of real-world healthcare data.

Architecture addresses privacy, bias, and explainability challenges.

Supports lifecycle management of AI models in healthcare environments.

Abstract

Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes. Despite the hype with AI, the full realization of this potential is seriously hindered by several systemic problems, including data privacy, security, bias, fairness, and explainability. In this paper, we propose a novel canonical architecture for the development of AI models in healthcare that addresses these challenges. This system enables the creation and management of AI predictive models throughout all the phases of their life cycle, including data ingestion, model building, and model promotion in production environments. This paper describes this architecture in detail, along with a qualitative evaluation of our experience of using it on real world problems.