Karandeep Singh picture

Karandeep Singh, MD, MMSc

Assistant Professor of Learning Health Sciences
Assistant Professor of Internal Medicine
Assistant Professor of Urology
Assistant Professor of Information
Accepting HILS PhD Students? Yes
Accepting PIBS Students? Yes


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

1. Singh K, Valley TS, Tang S, Li BY, Kamran F, Sjoding MW, Wiens J, Otles E,
Donnelly JP, Wei MY, McBride JP, Cao J, Penoza C, Ayanian JZ, Nallamothu BK.
Evaluating a Widely Implemented Proprietary Deterioration Index Model among
Hospitalized COVID-19 Patients. Ann Am Thorac Soc. 2020 Dec 24. doi:
10.1513/AnnalsATS.202006-698OC. Epub ahead of print. PMID: 33357088.
2. Singh K, Beam AL, Nallamothu BK. Machine Learning in Clinical Journals:
Moving From Inscrutable to Informative. Circ Cardiovasc Qual Outcomes. 2020
Oct;13(10):e007491. doi: 10.1161/CIRCOUTCOMES.120.007491. Epub 2020 Oct 14.
PMID: 33079583.
3. Singh K, Choudhry NK, Krumme AA, McKay C, McElwee NE, Kimura J, Franklin JM. A concept-wide association study to identify potential risk factors for
nonadherence among prevalent users of antihypertensives. Pharmacoepidemiol Drug
Saf. 2019 Oct;28(10):1299-1308. doi: 10.1002/pds.4850. Epub 2019 Jul 16. PMID:
4. Auffenberg GB, Ghani KR, Ramani S, Usoro E, Denton B, Rogers C, Stockton B,
Miller DC, Singh K; Michigan Urological Surgery Improvement Collaborative.
askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model
to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men. Eur
Urol. 2019 Jun;75(6):901-907. doi: 10.1016/j.eururo.2018.09.050. Epub 2018 Oct 11. PMID: 30318331; PMCID: PMC6459726.
5. Singh K, Drouin K, Newmark LP, Lee J, Faxvaag A, Rozenblum R, Pabo EA,
Landman A, Klinger E, Bates DW. Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain. Health Aff (Millwood). 2016 Dec 1;35(12):2310-2318. doi: 10.1377/hlthaff.2016.0578. PMID: 27920321.