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 is the associate workgroup director for the University's Precision Health Implementation Workgroup, which supports the implementation of campus-wide scientific discoveries into patient care. 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, 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.
Karandeep Singh teaches LHS 610: Exploratory Data Analysis for Health.
Areas of Interest
Research and scholarly interests: Natural language processing of clinical notes, risk prediction in health, mobile health apps, wearable technologies, learning health systems, population health
Subject-matter expertise: Health informatics, natural language processing, high-dimensional statistics and machine learning, R programming, chronic kidney disease
Published Articles or Reviews
- Singh K, Shah NH, Vickers AJ. Assessing the net benefit of machine learning models in the presence of resource constraints. J Am Med Inform Assoc. 2023 Mar 16;30(4):668–673. PMCID: PMC10018264. https://academic.oup.com/jamia/article-abstract/30/4/668/7051008.
- Cao J, Zhang X, Shahinian V, Yin H, Steffick D, Saran R, Crowley S, Mathis M, Nadkarni GN, Heung M, Singh K. Generalizability of an acute kidney injury prediction model across health systems. Nat Mach Intell. Springer Science and Business Media LLC; 2022 Dec 1;4(12):1121–1129. https://www.nature.com/articles/s42256-022-00563-8.
- Wong A, Otles E, Donnelly JP, Krumm A, McCullough J, DeTroyer-Cooley O, Pestrue J, Phillips M, Konye J, Penoza C, Ghous M, Singh K. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. JAMA Intern Med. 2021 Aug 1;181(8):1065–1070. PMCID: PMC8218233. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307.
- Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, Kohane IS, Saria S. The Clinician and Dataset Shift in Artificial Intelligence. N Engl J Med. 2021 Jul 15;385(3):283-286. doi: 10.1056/NEJMc2104626. PMID: 34260843; PMCID: PMC8665481. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665481/.