Machine-Learning in Medicine

Arthur Lee Samuel, an expert in the field of computer gaming and artificial intelligence, is credited as defining machine-learning as a "field of study that gives computers the ability to learn without being explicitly programmed." Through machine-learning, we are developing the complex models and algorithms, based on electronic health record data, to improve diagnostics and predict outcomes in patients with acute kidney injury and chronic kidney disease.
Bio Profile

Francis Perry Wilson, MD, MS

Assistant Professor of Medicine (Nephrology)

Research Interests

Activin Receptors, Type II; Acute Kidney Injury; Healthcare Disparities; Nephrology; Sarcopenia

Dr. Wilson grew up in Connecticut, before attending Harvard College where he graduated with honors in biochemistry. He then attended medical school at Columbia College of Physicians and Surgeons, before completing his internship, residency, and fellowship at the University of Pennsylvania. In 2012, he received a Masters degree in Clinical Epidemiology, which has informed his research ever since. At Yale since 2014, his goal is use patient-level data and advanced analytics to personalize...

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Aditya Biswas

Postgraduate Assistant, Yale Univeristy

Chess Stetson, PhD

Helynx, Inc.

Machine-Learning Publications

Contact Information

For more information, or if you are interested in collaborating on this study, please contact F. Perry Wilson

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Project Funding

Funding for this project comes, in part, from the following grant: 

K23 DK097201 

"Mediators & Prognostic Value of Muscle Mass & Function in Chronic Kidney Disease"