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在学习型医疗保健系统中实现个体化健康。

Enabling individualised health in learning healthcare systems.

机构信息

Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins Medicine, Baltimore, Maryland, USA.

Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.

出版信息

BMJ Evid Based Med. 2020 Aug;25(4):125-129. doi: 10.1136/bmjebm-2019-111190. Epub 2019 May 11.

Abstract

The rising burden of healthcare costs suggests that the healthcare system could benefit from novel methods that allow for continuous learning to provide more data-driven, individualised care at lower costs and with improved outcomes. Here, we present our synergistic Learning approach for Prediction, Interpretation/Inference and Communication (Learning PIC) framework to address the challenges hindering the successful implementation of learning healthcare systems and to enable the effective delivery of evidence-based medicine.

摘要

不断增加的医疗保健成本负担表明,医疗保健系统可能受益于新方法,这些方法允许持续学习,以更低的成本和更好的结果提供更具数据驱动性和个性化的护理。在这里,我们提出了我们的协同学习预测、解释/推理和通信(Learning PIC)框架方法,以解决阻碍学习型医疗保健系统成功实施的挑战,并实现循证医学的有效交付。

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