Member Database

Karandeep Singh

Assistant Professor
Internal Medicine/Learning Health Sciences
Medicine

Karandeep Singh, MD, MMSc, is an Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information at the University of Michigan. He is a nephrologist with a background in biomedical informatics who uses machine learning methods to model electronic health record and registry data in support of a learning health system. He directs the Machine Learning for Learning Health Systems lab, which focuses on using machine learning and biomedical informatics methods to understand and improve health at scale. His research spans multiple clinical domains, including nephrology, urology, emergency medicine, obstetrics, and ophthalmology. He chairs the Michigan Medicine Clinical Intelligence Committee, which focuses on implementation of machine learning models across the health system. He teaches a graduate-level health data science course. 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. 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.


Projects:

Funded 2019 Investigators Award -- "Developing an Early Warning System for Treatment of Postpartum Hemmorhage Using Time-Series Machine Learning Models", R01DK133226 Cardiac sURgery anesthesia Best practices to reduce Acute Kidney Injury (CURB-AKI), workgroup leader-health implementation

Research Area(s)

Postpartum hemmorhage

Grants

  • Co-investigator of: Training to Advance Care Through Implementation Science in Cardiac And Lung Illnesses (TACTICAL)
  • Principal investigator of: SIM Predictive Model Development
  • Co-investigator of: Predictive Analytic and Geospatial Approaches to Enable Targeted Prevention and Slowing of the Progression of Kidney Disease among US Veterans
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