论文标题
纵向患者记录的预测分析的规范结构
A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
论文作者
论文摘要
医疗保健生态系统中的许多机构正在对AI技术进行大量投资，以较低的成本优化其业务运营，并改善患者的结果。尽管AI进行了炒作，但这种潜力的全部实现受到了一些系统性问题的严重阻碍，包括数据隐私，安全性，偏见，公平性和解释性。在本文中，我们提出了一种新型的规范体系结构，以开发医疗保健中的AI模型，以应对这些挑战。该系统可以在其生命周期的所有阶段中创建和管理AI预测模型，包括数据摄入，模型构建和模型促进。本文详细描述了这种体系结构，以及对我们在现实世界中使用它的经验的定性评估。
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.
