A Canonical Architecture For Predictive Analytics on Longitudinal
Patient Records
- URL: http://arxiv.org/abs/2007.12780v2
- Date: Tue, 5 Jan 2021 19:46:04 GMT
- Title: A Canonical Architecture For Predictive Analytics on Longitudinal
Patient Records
- Authors: Parthasarathy Suryanarayanan, Bhavani Iyer, Prithwish Chakraborty,
Bibo Hao, Italo Buleje, Piyush Madan, James Codella, Antonio Foncubierta,
Divya Pathak, Sarah Miller, Amol Rajmane, Shannon Harrer, Gigi Yuan-Reed,
Daby Sow
- Abstract summary: We propose a novel canonical architecture for the development of AI models in healthcare.
This system enables the creation and management of AI predictive models throughout all the phases of their life cycle.
- Score: 7.168190535169044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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