Samer Albahra, M.D.

Samer Albahra, M.D.


Pathology - Clinical

Clinical Informatics


  • Health Sciences Assistant Clinical Professor


To see if Samer Albahra is accepting new patients, or for assistance finding a UC Davis doctor, please call 800-2-UCDAVIS (800-282-3284).

Pathology Building

4400 V St.
Sacramento, CA 95817
Driving Directions

Primary Phone:

Additional Phone Numbers

Physician Referrals: 800-4-UCDAVIS (800-482-3284)

Social Networking


Philosophy of Care

Provide the best care possible to all his patients.

Clinical Interests

Dr. Albahra is a clinical pathologist with informatics and machine learning expertise. His clinical areas of interest are clinical chemistry, toxicology, and clinical informatics.

Research/Academic Interests

Dr. Albahra has an extensive background in software engineering and machine learning to create tools that improve patient care.


Clinical Pathology


M.D., Ross University School of Medicine, Portsmouth, Dominica, West Indies 2012

B.A., Austin College, Sherman TX 2008


AP/CP Pathology, University of Texas Health Science Center, San Antonio TX 2013-2017


Clinical Informatics, UC Davis, Sacramento CA 2019-2021

Board Certifications

American Board of Pathology, Clinical Pathology,

Professional Memberships

American Association for Clinical Chemistry

American Medical Association (AMA)

American Medical Informatics Association

American Society for Clinical Pathology

College of American Pathologists

Select Recent Publications

Rashidi HH, Tran N, Albahra S, Dang LT. Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML. Int J Lab Hematol. 2021 Jul;43 Suppl 1:15-22. doi:10.1111/ijlh.13537. PMID:34288435.

Rashidi HH, Makley A, Palmieri TL, Albahra S, Loegering J, Fang L, Yamaguchi K, Gerlach T, Rodriquez D, Tran NK. Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing. Arch Pathol Lab Med. 2021 Mar 1;145(3):320-326. doi:10.5858/arpa.2020-0110-OA. PMID:33635951.

Jen KY, Albahra S, Yen F, Sageshima J, Chen LX, Tran N, Rashidi HH. Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts. Transplantation. 2021 Feb 4. doi:10.1097/TP.0000000000003640. Epub ahead of print. PMID:33560727.

Tran NK, Albahra S, Pham TN, et al. Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept. Sci Rep. 2020 Jul;10(1):12354. doi:10.1038/s41598-020-69433-w.