Recent Publications

Peer-reviewed articles by Computational Precision Health faculty and students (select list).

Alderden, J., Johnny, J., Brooks, K. R., Wilson, A., Yap, T. L., Zhao, Y. L., van der Laan, M., & Kennerly, S. (2024). Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk. American journal of critical care : an official publication, American Association of Critical-Care Nurses, 33(5), 373–381. 

Holmgren, A. J., Hendrix, N., Maisel, N., Everson, J., Bazemore, A., Rotenstein, L., … & Adler-Milstein, J. (2024). Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians. JAMA Network Open, 7(8), e2426956-e2426956.

Kundu, S., Sair, H., Sherr, E. H., Mukherjee, P., & Rohde, G. K. (2024). Discovering the gene-brain-behavior link in autism via generative machine learning. Science Advances, 10(24), eadl5307. 

Deshpande, D., Chhugani, K., Ramesh, T., Pellegrini, M., Shiffman, S., Abedalthagafi, M. S., Alqahtani, S., Ye, J., Liu, X. S., Leek, J. T., Brazma, A., Ophoff, R. A., Rao, G., Butte, A. J., Moore, J. H., Katritch, V., & Mangul, S. (2024). The evolution of computational research in a data-centric world. Cell, 187(17), 4449–4457. 

Zink, A., Obermeyer, Z., & Pierson, E. (2024). Race adjustments in clinical algorithms can help correct for racial disparities in data quality. Proceedings of the National Academy of Sciences of the United States of America, 121(34), e2402267121.

Franklin, J. B., Marra, C., Abebe, K. Z., Butte, A. J., Cook, D. J., Esserman, L., Fleisher, L. A., Grossman, C. I., Kass, N. E., Krumholz, H. M., Rowan, K., Abernethy, A. P., & JAMA Summit on Clinical Trials Participants (2024). Modernizing the Data Infrastructure for Clinical Research to Meet Evolving Demands for Evidence. JAMA, 10.1001/jama.2024.0268. 

Chow, R., Hasan, S., Zheng, A., Gao, C., Valdes, G., Yu, F., … & Simone II, C. B. (2024). The Accuracy of Artificial Intelligence ChatGPT in Oncology Exam Questions. Journal of the American College of Radiology. 

Nance, N., Petersen, M. L., van der Laan, M., & Balzer, L. B. (2024). The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications. Epidemiology, 10-1097. 

Tang, A. S., Woldemariam, S. R., Miramontes, S., Norgeot, B., Oskotsky, T. T., & Sirota, M. (2024). Harnessing EHR data for health research. Nature Medicine, 1-9. 

Deng, D., Ostrem, J. L., Nguyen, V., Cummins, D. D., Sun, J., Pathak, A., … & Abbasi-Asl, R. (2024). Interpretable video-based tracking and quantification of parkinsonism clinical motor states. npj Parkinson’s Disease, 10(1), 122.

Sushil, M., Zack, T., Mandair, D., Zheng, Z., Wali, A., Yu, Y. N., … & Butte, A. J. (2024). A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports. Journal of the American Medical Informatics Association, ocae146.

Ong, J. C. L., Chang, S. Y. H., William, W., Butte, A. J., Shah, N. H., Chew, L. S. T., … & Ting, D. S. W. (2024). Medical Ethics of Large Language Models in Medicine. NEJM AI, AIra2400038. 

Beecy, A. N., Longhurst, C. A., Singh, K., Wachter, R. M., & Murray, S. G. (2024). The Chief Health AI Officer—An Emerging Role for an Emerging Technology. NEJM AI, AIp2400109. 

Feng, J., Gossmann, A., Pirracchio, R., Petrick, N., Pennello, G. A., & Sahiner, B. (2024, April). Is this model reliable for everyone? Testing for strong calibration. In International Conference on Artificial Intelligence and Statistics (pp. 181-189). PMLR.