Mayo Clinic & nference AI Challenge program winners

The Mayo Clinic Office of Translation to Practice (part of the Center for Clinical and Translational Science), in collaboration with Mayo Clinic Ventures, Mayo Clinic Health Sciences Research, Mayo Clinic Platform and nference, announces the inaugural projects selected for the Mayo Clinic and nference AI Challenge program.

This program supports the development and maturation of innovations in artificial/augmented intelligence (AI) and machine learning as applied to clinically relevant challenges.

The following projects were selected to receive 2021 funding, along with support from a customized project development team:

Alina Allen, M.D. (GI ’14, HEPT ’15), Division of Gastroenterology and Hepatology, Mayo Clinic in Rochester, "Mayo Model for Risk-Stratification and Management of Nonalcoholic Fatty Liver Disease"

Mohamad Bydon, M.D. (NS ’15), Department of Neurologic Surgery, Mayo Clinic in Rochester, "A Natural Language Processing Platform for Real-time, Patient-Specific Venous Thromboembolism Risk Stratification in the Postoperative Period"

Wisit Cheungpasitporn, M.D. (CTSA ’15, NEPH ’16, RNTX ’17), Division of Nephrology and Hypertension, Mayo Clinic in Rochester, "Prediction of Acute Kidney Injury Among Patients With Chronic Kidney Disease by Machine Learning"

Suraj Kapa, M.D. (I ’09), Division of Heart Rhythm Services, Department of Cardiovascular Medicine, Mayo Clinic in Rochester, "Machine-Assisted Knowledge Elicitation to Inform Data Inputs for Clinical Decision Models"

Bradley Leibovich, M.D. (U ’01), Department of Urology, Mayo Clinic in Rochester, "Deep Learning to Characterize Oncologic Potential of Renal Masses"   

About the Mayo Clinic and nference AI Challenge

In response to the elevated demand for new data technologies, and internal support of these capabilities, the Mayo Clinic and nference AI Challenge supports innovative research in data science and AI.

This program seeks research projects targeting novel AI and data science applications solving clinical or technical challenges. Projects are evaluated primarily on the basis of the medical need of the problem to be addressed, as well as the potential for AI to make a meaningful impact on that problem. Consultation with representatives of nference and Health Sciences Research is available and encouraged.

 

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