November 2024 SOCRA Source Journal - Journal - Page 20
References (continued)
Park, S. H., Choi, J. I., Fournier, L., & Vasey, B. (2022). Randomized clinical trials of arti昀椀cial
intelligence in medicine: why, when, and how?. Korean Journal of Radiology, 23(12), 1119.
https://doi.org/10.3348%2Fkjr.2022.0834.
Piette, J. D., Newman, S., Krein, S. L., Marinec, N., Chen, J., Williams, D. A., ... & Heapy, A.
A. (2022). Arti昀椀cial Intelligence (AI) to improve chronic pain care: Evidence of AI learning.
Intelligence-Based Medicine, 6, 100064. https://doi.org/10.1016/j.ibmed.2022.100064.
Qi, M., Cahan, O., Foreman, M. A., Gruen, D. M., Das, A. K., & Bennett, K. P. (2021). Quantifying
representativeness in randomized clinical trials using machine learning fairness metrics. JAMIA
open, 4(3), ooab077. https://doi.org/10.1093/JAMIAOPEN/OOAB077.
Sangari, N., & Qu, Y. (2020, December). A comparative study on machine learning algorithms for
predicting breast cancer prognosis in improving clinical trials. In 2020 International conference on
computational science and computational intelligence (CSCI) (pp. 813-818). IEEE.doi:https://doi.
org/10.1109/CSCI51800.2020.00152.
Schperberg, A. V., Boichard, A., Tsigelny, I. F., Richard, S. B., & Kurzrock, R. (2020). Machine
learning model to predict oncologic outcomes for drugs in randomized clinical trials. International
journal of cancer, 147(9), 2537-2549. https://doi.org/10.1002/IJC.33240.
Shah, P., Mishra, D., Shanmugam, M., Vighnesh, M. J., & Jayaraj, H. (2022). Acceptability
of arti昀椀cial intelligence-based retina screening in general population. Indian Journal of
Ophthalmology, 70(4), 1140. https://doi.org/10.4103%2Fijo.IJO_1840_21.
Shivade, C., Hebert, C., Regan, K., Fosler-Lussier, E., & Lai, A. M. (2016). Automatic data source
identi昀椀cation for clinical trial eligibility criteria resolution. In AMIA Annual Symposium Proceedings
(Vol. 2016, p. 1149). American Medical Informatics Association. http://dx.doi.org/pmc/articles/
PMC5333255/.
Siah, K. W., Khozin, S., Wong, C. H., & Lo, A. W. (2019). Machine-learning and stochastic tumor
growth models for predicting outcomes in patients with advanced non–Small-Cell lung cancer.
JCO clinical cancer informatics, 1, 1-11. https://doi.org/10.1200/CCI.19.00046.
van der Lee, M., & Swen, J. J. (2023). Arti昀椀cial intelligence in pharmacology research and practice.
Clinical and Translational Science, 16(1), 31-36. https://doi.org/10.1111/cts.13431.
Vazquez, J., Abdelrahman, S., Byrne, L. M., Russell, M., Harris, P., & Facelli, J. C. (2021). Using
supervised machine learning classi昀椀ers to estimate likelihood of participating in clinical trials of a
de-identi昀椀ed version of ResearchMatch. Journal of Clinical and Translational Science, 5(1), e42.
https://doi.org/10.1017/CTS.2020.535.
Vydiswaran, V. V., Strayhorn, A., Zhao, X., Robinson, P., Agarwal, M., Bagazinski, E., ... & Yuan, N.
(2019). Hybrid bag of approaches to characterize selection criteria for cohort identi昀椀cation.
Journal of the American Medical Informatics Association, 26(11), 1172-1180. https://doi.
org/10.1093/JAMIA/OCZ079.
20
SOCRA SOURCE © November 2024