A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records

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 probl...

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Bibliographic Details
Published in:arXiv.org 2021-01
Main Authors: Parthasarathy Suryanarayanan, Iyer, Bhavani, Chakraborty, Prithwish, Bibo Hao, Buleje, Italo, Madan, Piyush, Codella, James, Foncubierta, Antonio, Pathak, Divya, Miller, Sarah, Rajmane, Amol, Harrer, Shannon, Yuan-Reed, Gigi, Sow, Daby
Format: Article
Language:English
Subjects:
Architecture
Health care
Ingestion
Prediction models
Online Access:Get full text
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Summary: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.
ISSN:2331-8422