Goldstein, Benjamin A

Large-scale association analysis identifies new risk loci for coronary artery disease

The CARDIoGRAMplusC4D Consortium, Deloukas, P., DIAGRAM Consortium, CARDIOGENICS Consortium, MuTHER Consortium, Wellcome Trust Case Control Consortium, Kanoni, S., Willenborg, C., Farrall, M., Assimes, T. L., et al. (2013) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 1,614

Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review

Goldstein, B. A., Navar, A. M., Pencina, M. J., & Ioannidis, J. P. A. (2017) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 936

Investigating sources of inaccuracy in wearable optical heart rate sensors

Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020) · npj Digital Medicine

Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic Control

Cited by 673

Minimal Impact of Implemented Early Warning Score and Best Practice Alert for Patient Deterioration*

Bedoya, A. D., Clement, M. E., Phelan, M., Steorts, R. C., O’Brien, C., & Goldstein, B. A. (2019) · Critical Care Medicine

Healthcare Technology and Patient MonitoringElectronic Health Records Systems

Cited by 162

Genome-wide association study of severity in multiple sclerosis

International Multiple Sclerosis Genetics Consortium, Briggs, F. B. S., Shao, X., Goldstein, B. A., Oksenberg, J. R., Barcellos, L. F., & De Jager, P. L. (2011) · Genes & Immunity

Multiple Sclerosis Research StudiesGenetic Associations and Epidemiology

Cited by 120

A framework for the oversight and local deployment of safe and high-quality prediction models

Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O’Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., et al. (2022) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 119

Clinician burnout associated with sex, clinician type, work culture, and use of electronic health records

McPeek-Hinz, E., Boazak, M., Sexton, J. B., Adair, K. C., West, V., Goldstein, B. A., Alphin, R. S., Idris, S., Hammond, W. E., Hwang, S. E., et al. (2021) · JAMA Network Open

Healthcare professionals’ stress and burnoutElectronic Health Records Systems

Cited by 80

Documenting or operating: where is time spent in general surgery residency?

Cox, M. L., Farjat, A. E., Risoli, T., Peskoe, S., Goldstein, B. A., Turner, D. A., & Migaly, J. (2018) · Journal of Surgical Education

Electronic Health Records SystemsHospital Admissions and Outcomes

Cited by 68

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare

Economou-Zavlanos, N. J., Bessias, S., Cary, M. P., Bedoya, A. D., Goldstein, B. A., Jelovsek, J. E., O’Brien, C. L., Walden, N., Elmore, M., Parrish, A. B., et al. (2024) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 57

Reply: Matters Arising ‘Investigating sources of inaccuracy in wearable optical heart rate sensors’

Bent, B., Enache, O. M., Goldstein, B., Kibbe, W., & Dunn, J. P. (2021) · npj Digital Medicine

Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic Control

Cited by 17