Recent Publications
Peer-reviewed articles by Computational Precision Health faculty (select list)
Abràmoff, M. D., Tarver, M. E., Loyo-Berrios, N., Trujillo, S., Char, D., Obermeyer, Z., … & Maisel, W. H. (2023). Considerations for addressing bias in artificial intelligence for health equity. NPJ digital medicine, 6(1), 170.
Alaa, A., Ahmad, Z., & van der Laan, M. (2023). Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. arXiv e-prints, arXiv-2308.
Butte, A.J., 2023. Artificial Intelligence—From Starting Pilots to Scalable Privilege. JAMA oncology. Published online August 24, 2023. doi:10.1001/jamaoncol.2023.2867
Chang, J. H., Lin, A., Singer, L., Mohamad, O., Chan, J., Friesner, I., … & Hong, J. C. (2023). Identifying Common Topics in Patient Portal Messages with Unsupervised Natural Language Processing. International Journal of Radiation Oncology* Biology* Physics, 117(2), e460-e461.
Chugh, R., Liu, A. W., Idomsky, Y., Bigazzi, O., Maiorano, A., Medina, E., Pierce, L., Odisho, A., & Mahadevan, U. (2023). A Digital Health Intervention to Improve the Clinical Care of Inflammatory Bowel Disease Patients. Applied clinical informatics, 10.1055/a-2154-9172. Advance online publication. https://doi.org/10.1055/a-2154-9172
Linfield, G. H., Patel, S., Ko, H. J., Lacar, B., Gottlieb, L. M., Adler-Milstein, J., … & De Marchis, E. H. (2023). Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review. Health Informatics Journal, 29(3), 14604582231200300
Mikhael, P.G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A.K., Bourgouin, P.P., Chan, P., Mrah, S., Amayri, W. and Juan, Y.H., 2023. Sybil: A validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. Journal of Clinical Oncology, 41(12), pp.2191-2200.
Yala, A., & Hughes, K. S. (2023). Rethinking Risk Modeling with Machine Learning. Annals of Surgical Oncology, 1-3.
Zamirpour, S., Hubbard, A. E., Feng, J., Butte, A. J., Pirracchio, R., & Bishara, A. (2023). Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors. Bioengineering, 10(8), 932.Zou, J., Gichoya, J. W., Ho, D. E., & Obermeyer, Z. (2023). Implications of predicting race variables from medical images. Science, 381(6654), 149-150.
