Biography

Research Interests: Biomedical data science/artificial intelligence for precision medicine: multimodal data analysis, federated computing, agent-based modeling, computer vision

Clinical Interests: Interventional Radiology, Radiation Oncology, Cardiology, Pathology

Eric is a student in the Medical Scientist Training Program (MD-PhD) at the Oregon Health and Science University (OHSU), and conducts his studies in the Quantitative Bioimaging Lab mentored by Dr. Young Hwan Chang. Prior to starting medical and graduate school, Eric worked in research groups at the Santa Clara Valley Medical Center (San Jose, CA), Institut Curie (Paris, France), and Stanford University (Palo Alto, CA). His work has focused on investigating links between umbilical cord biomarkers with neonatal body composition; developing statistical and machine learning models for predicting and treating chronic pain; and single cell analysis of the tumor microenvironment.

Eric received his BS in Biomedical Computation from the Stanford University School of Engineering and MS in Biomedical Informatics from the Stanford School of Medicine. While an undergraduate student, Eric balanced competing for four years on the NCAA Division I wrestling team with tutoring chemistry and statistics, and leading excursions for Stanford Outdoor Education. At OHSU, he is a founding member of the Computation in Medicine Student Interest Group, an OASIS peer tutor, and a mentor for several of OHSU's mentoring programs. Eric also currently serves as co-chair for the Technology Committee of the American Physician Scientists Association (APSA).

When he is not busy learning about science or medicine, Eric likes to train for triathlons, travel, and spend time with friends and family.

Selected publications

1. Cramer, E., Seneviratne, M., Sharifi, H., Ozturk, A. & Boussard, T. Predicting the Incidence of
Pressure Ulcers in the Intensive Care Unit Using Machine Learning. eGEMs (Generating Evidence &
Methods to improve patient outcomes). doi:https://doi.org/10.5334/egems.307 (2019).

2. Hah, J., Cramer, E., Carroll, I. & Mackey, S. Acute Pain Predictors of Postoperative Pain Resolution,
Opioid Cessation, and Recovery: Secondary Analysis of the START Randomized Clinical Trial. Journal of the American Medical Association Open Network. doi:doi:10.1001/jamanetworkopen.2019.0168. https://www.ncbi.nlm.nih.gov/pubmed/30821824 (2019).

3. Gilam, G. et al. Classifying chronic pain using multidimensional pain-agnostic symptom assessments
and clustering analysis. Science Advances 7, eabj0320 (2021).

4. Hah, J. M., Nwaneshiudu, C. A., Cramer, E. M., Carroll, I. R. & Curtin, C. M. Acute Pain Predictors of Remote Postoperative Pain Resolution After Hand Surgery. Pain and Therapy. doi:10.1007/s40122-021-00263-y. https://doi.org/10.1007/s40122-021-00263-y (Apr. 2021).

5. Mardian, A. et al. Engagement in Prescription Opioid Tapering Research: the EMPOWER Study and a Coproduction Model of Success. Journal of General Internal Medicine. doi:10.1007/s11606-021-07085-w. https://doi.org/10.1007/s11606-021-07085-w (Aug. 2021).

6. Scherrer, K. H. et al. Development and validation of the Collaborative Health Outcomes Information Registry body map. PAIN Reports 6, e880 (Jan. 2021).

7. Cramer, E., Ziadni, M., Scherrer, K. H., Mackey, S. & Kao, M.-C. CHOIRBM: An R package for exploratory data analysis and interactive visualization of pain patient body map data. PLOS Computational Biology 18 (ed Marz, M.) e1010496 (Oct. 2022).

8. Ziadni, M. S. et al. The impact of COVID-19 on patients with chronic pain seeking care at a tertiary pain clinic. Scientific Reports 12. doi:10.1038/s41598- 022- 10431- 5. https://doi.org/10.1038/s41598-022-10431-5 (Apr. 2022).