Kefeli, J., & Tatonetti, N. (2024). TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models. Patterns, 5(3), 100933. https://doi.org/10.1016/j.patter.2024.100933

Ramlall, V., Gisladottir, U., Kefeli, J., Tanaka, Y., May, B., & Tatonetti, N. (2023). Using machine learning probabilities to identify effects of COVID-19. Patterns, 4(12), 100889. https://doi.org/10.1016/j.patter.2023.100889

Park, J., Artin, M. G., Lee, K. E., May, B. L., Park, M., Hur, C., & Tatonetti, N. P. (2023). Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer. Patterns, 4(1), 100636. https://doi.org/10.1016/j.patter.2022.100636

Ramirez, A. H., Sulieman, L., Schlueter, D. J., Halvorson, A., Qian, J., Ratsimbazafy, F., Loperena, R., Mayo, K., Basford, M., Deflaux, N., Muthuraman, K. N., Natarajan, K., Kho, A., Xu, H., Wilkins, C., Anton-Culver, H., Boerwinkle, E., Cicek, M., Clark, C. R., … Zwick, M. E. (2022). The All of Us Research Program: Data quality, utility, and diversity. Patterns, 3(8), 100570. https://doi.org/10.1016/j.patter.2022.100570

Rogozhnikov, A., Ramkumar, P., Bedi, R., Kato, S., & Escola, G. S. (2022). Hierarchical confounder discovery in the experiment-machine learning cycle. Patterns, 3(4), 100451. https://doi.org/10.1016/j.patter.2022.100451