Samer Albahra, M.D.
Specialties
Pathology - Clinical
Clinical Informatics
Department
Title
- Health Sciences Assistant Clinical Professor
Reviews
Pathology Building
4400 V St.
Sacramento, CA 95817
Driving Directions
Primary Phone:
916-734-2525
Additional Phone Numbers
Physician Referrals: 800-4-UCDAVIS (800-482-3284)
Social Networking
Blog:
https://albahra.com
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.
Division
Clinical Pathology
Education
M.D., Ross University School of Medicine, Portsmouth, Dominica, West Indies 2012
B.A., Austin College, Sherman TX 2008
Residency
AP/CP Pathology, University of Texas Health Science Center, San Antonio TX 2013-2017
Fellowships
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.
