Machine Learning for Learning Health Systems

Welcome to the ML4LHS Lab home page.

The Machine Learning for Learning Health Systems (ML4LHS) Lab focuses on translational issues related to the implementation of machine learning (ML) models within health systems. Effective translation of ML in clinical care requires attention to model development, evaluation, and implementation, and it needs to consider clinical workflows, resource constraints, and the practice of medicine more broadly. Our lab is geared towards studying these issues in applied settings, sharing software tools to foster reproducibility, and in using our findings to guide clinicians, health systems, and policy.

Biography

Karandeep Singh, MD, MMSc, is an Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information at the University of Michigan. He directs the Machine Learning for Learning Health Systems (ML4LHS) Lab, which focuses on translational issues related to the implementation of machine learning models within health systems. He serves as an Associate Chief Medical Information Officer of Artificial Intelligence for Michigan Medicine and is the Associate Director for Implementation for Precision Health at the University of Michigan. He teaches a health data science course for graduate and doctoral students, and provides clinical care for people with kidney disease.

He completed his internal medicine residency at UCLA Medical Center, where he served as chief resident, and a nephrology fellowship in the combined Brigham and Women’s Hospital/Massachusetts General Hospital program in Boston, MA. He completed his medical education at the University of Michigan Medical School and holds a master’s degree in medical sciences in Biomedical Informatics from Harvard Medical School. He is board certified in internal medicine, nephrology, and clinical informatics.

His work in machine learning and digital health has been published in leading journals in their respective fields, including the New England Journal of Medicine, Lancet, British Medical Journal, JAMA Internal Medicine, Nature Machine Intelligence, Health Affairs, Clinical Journal of the American Society of Nephrology, Ophthalmology, Radiology, and European Urology.