Published in Nature Communications, 2024
Citation: Rajendran, S., Brendel, M., Barnes, J. et al. Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging. Nat Commun 15, 7756 (2024). https://doi.org/10.1038/s41467-024-51823-7
Published in Nature Communications, 2024
Citation: Mandracchia, B., Zheng, C., Rajendran, S. et al. High-speed optical imaging with sCMOS pixel reassignment. Nat Commun 15, 4598 (2024). https://doi.org/10.1038/s41467-024-48987-7
Published in Cell Patterns, 2024
Citation: Rajendran, S., Pan, W., Sabuncu, R. M., Chen, Y., Zhou, J., & Wang, F. (2023). Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation. Patterns, 100913. https://doi.org/10.1016/j.patter.2023.100913
Published in Cell Patterns, 2024
Citation: Pan, W., Xu, Z., Rajendran, S., & Wang, F. (2023). An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals. Patterns (New York, N.Y.), 5(1), 100898. https://doi.org/10.1016/j.patter.2023.100898
Published in Human Reproduction, 2023
Citation: S Rajendran and others, O-120 Predicting Embryo Ploidy Status Using Time-lapse Images, Human Reproduction, Volume 38, Issue Supplement_1, June 2023, dead093.147, https://doi.org/10.1093/humrep/dead093.147
Published in Journal of Medical Internet Research, 2023
Citation: Cao, Y., Rajendran, S., Sundararajan, P., Law, R., Bacon, S., Sumner, S. A., & Masuda, N. (2023). Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative Study. Journal of medical Internet research, 25, e45171. https://doi.org/10.2196/45171
Published in Iscience, 2023
Citation: Su, C., Hou, Y., Zhou, M., Rajendran, S., Maasch, J. R. M. A., Abedi, Z., Zhang, H., Bai, Z., Cuturrufo, A., Guo, W., Chaudhry, F. F., Ghahramani, G., Tang, J., Cheng, F., Li, Y., Zhang, R., DeKosky, S. T., Bian, J., & Wang, F. (2023). Biomedical discovery through the integrative biomedical knowledge hub (iBKH). iScience, 26(4), 106460. https://doi.org/10.1016/j.isci.2023.106460
Published in PLOS Digital Health, 2023
Citation: Rajendran, S., Xu, Z., Pan, W., Ghosh, A., & Wang, F. (2023). Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care. PLOS digital health, 2(3), e0000117. https://doi.org/10.1371/journal.pdig.0000117
Published in The Lancet Digital Health, 2023
Citation: Barnes, J., Brendel, M., Gao, V. R., Rajendran, S., Kim, J., Li, Q., Malmsten, J. E., Sierra, J. T., Zisimopoulos, P., Sigaras, A., Khosravi, P., Meseguer, M., Zhan, Q., Rosenwaks, Z., Elemento, O., Zaninovic, N., & Hajirasouliha, I. (2023). A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. The Lancet. Digital health, 5(1), e28–e40. https://doi.org/10.1016/S2589-7500(22)00213-8
Published in Cureus, 2022
Citation: Pullen, M. W., Valero-Moreno, F., Rajendran, S., Shah, V. U., Bruneau, B. R., Martinez, J. L., Ramos-Fresnedo, A., Quinones-Hinojosa, A., & Fox, W. C. (2022). Creation of a Proof-of-Concept 3D-Printed Spinal Lateral Access Simulator. Cureus, 14(5), e25448. https://doi.org/10.7759/cureus.25448
Published in Society of Critical Care Medicine, 2022
Citation: Rajendran, S. , Ong, T. , Zameza, P. , Wolfe, S. , Topaloglu, U. , Duncan, P. , Anwar, M. , Samuel, R. , Budigi, B. , Lack, C. & Sarwal, A. (2022). 779: DISTANCE TRAVELED TO TERTIARY CARE AS PROGNOSTIC INDICATOR IN INTRACEREBRAL HEMORRHAGE OUTCOMES. Critical Care Medicine, 50 (1), 384-384. doi: 10.1097/01.ccm.0000809440.55714.3d.
Published in CrimRxiv, 2021
Citation: Rajendran, S., & Sundararajan, P. (2021). Predicting criminal recidivism using specialized feature engineering and XGBoost. CrimRxiv. https://doi.org/10.21428/cb6ab371.d95f8c48
Published in Frontiers in Artificial Intelligence, 2021
Citation: Topaloglu MY, Morrell EM, Rajendran S, Topaloglu U. In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare. Front Artif Intell. 2021 Oct 6;4:746497. doi: 10.3389/frai.2021.746497. PMID: 34693280; PMCID: PMC8528445.
Published in JCO Clinical Cancer Informatics, 2021
Citation: Rajendran S, Obeid JS, Binol H, D Agostino R Jr, Foley K, Zhang W, Austin P, Brakefield J, Gurcan MN, Topaloglu U. Cloud-Based Federated Learning Implementation Across Medical Centers. JCO Clin Cancer Inform. 2021 Jan;5:1-11. doi: 10.1200/CCI.20.00060. PMID: 33411624; PMCID: PMC8140794.
Published in AMIA Summits on Translational Science Proceedings, 2020
Citation: Rajendran S, Topaloglu U. Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning. AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:507-516. PMID: 32477672; PMCID: PMC7233082.
Published in 2020 ASCO Annual Meeting, 2020
Citation: Margalski, Daniel, et al. “Machine Learning for Prospective Identification of Immunotherapy Related Adverse Events (Iraes).” Journal of Clinical Oncology, vol. 38, no. 15_suppl, 2020, https://doi.org/10.1200/jco.2020.38.15_suppl.e14064.