ASN's Mission

ASN leads the fight to prevent, treat, and cure kidney diseases throughout the world by educating health professionals and scientists, advancing research and innovation, communicating new knowledge, and advocating for the highest quality care for patients.

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Kidney Week

Please note that registration for Advances in Research Conference: Machine Learning and Kidney Diseases is now sold out.

Advances in Research Conference: Machine Learning and Kidney Diseases

Course Description

Spurred by rapid advances in machine-learning theory as well as the hardware capacity of modern computing devices, a new era of scientific inquiry can use the tools of computer science to ask and answer meaningful questions about biology. But the fields of machine learning, artificial intelligence, systems biology, and big data analytics are plagued by misunderstanding, hype, and jargon that may seem impenetrable to clinicians.

In this program, luminaries in the field of computational biology and machine learning discuss the ongoing machine-learning and systems biology revolution, with an eye toward the role of clinicians and researchers. Attendees gain an understanding of key principles, terms, and applications related to machine learning and a sense of the opportunities to improve clinical care and research in the area. Discussion includes the promise of this new technology as well as its limitations and the ethical concerns that arise when computing power of this magnitude is brought to bear on individual patient data.

ASN designates this live activity for a maximum of 16.5 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

This program includes a printed syllabus (not an electronic syllabus).

Course Objective(s)

Upon completion of the program, the participant will be able to: 1) describe the underpinnings of machine learning and systems biology and how these tools can be used to advance kidney research; 2) discuss the key components of machine-learning algorithms and some appropriate examples; 3) explain implementation of this approach across retrospective and prospective studies in nephrology; and 4) envision the next steps to expand on the early successes of machine learning in the biomedical field.

Course Chair(s)

  • Matthias Kretzler, MD
  • Olga Troyanskaya, PhD
  • Francis Perry Wilson, MD, MS


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