Achieving a Nationwide Learning Health System

By C. Friedman, A. Wong, D. Blumenthal. Science Translational Medicine, 2010

492 citations recorded by the source corpus.

Subfields: Health Information Management, Information Systems and Management, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/ff44691a8f96d7d35599c0f021a45d9241beddaa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Dmitriy Dligach
2. Timothy Miller
3. Dianbo Liu
4. Kenneth D. Mandl

In Information Systems and Management

1. Bingsheng He
2. Kenneth D. Mandl
3. Timothy Miller
4. Zeyi Wen
5. Zhaomin Wu
6. Dianbo Liu
7. Qinbin Li

In Public Health, Environmental and Occupational Health

1. Kenneth D. Mandl
2. Timothy Miller
3. Dianbo Liu

Citations in the corpus

• Machine learning in medicine: a practical introduction (2019, background): “The result will be a continuous source of data-driven insights to optimise biomedical research, public health, and health care quality improvement [10].”
• Two-stage Federated Phenotyping and Patient Representation Learning (2019, background): “The ability to utilize clinical notes distributed at different healthcare providers not only benefits a specific clinical practice task but also facilitates building a learning healthcare system, in which meaningful use of knowledge in distributed clinical notes will speed up progression of medical ”
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “ould be vouchers or additional service. 7.2 Healthcare Modern health systems require a cooperation among research institutes, hospitals, federal agencies in order to improve health care of the nation [61]. Moreover, a collaborative research among countries is vital when facing global health emergenc”
• Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine (2015, background): “It now takes 17 years for a laboratory discovery to reach widespread clinical application [8].”
• Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence (2019, methodology): “To accelerate a scalable and collaborative rapid learning health system ​(Friedman et al. 2010; Mandl et al. 2014)​, we propose a confederated machine learning method that trains machine learning models on data both horizontally and vertically separated by jointly learning a high level representatio”
• Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research (2013, unknown): “This investment sets the foundation for a “learning” health care system that facilitates clinical research, quality improvement, and other data-driven efforts to improve health.(5,6) At the same time, there has also been substantial federal investment in comparative effectiveness research (CER) that”
• Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach (2016, unknown): “achieving a learning health care system [18].”