Computational Precision Health

Recent Publications

Research

Recent Publications

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

Odisho, A. Y., Liu, A. W., Pace, W. A., Krumm, R., Cowan, J. E., Carroll, P. R., & Cooperberg, M. R. (2024). MP07-14 DEVELOPMENT OF A GENERATIVE ARTIFICIAL INTELLIGENCE DATA PIPELINE TO AUTOMATE THE CAPTURE OF UNSTRUCTURED MRI DATA FOR PROSTATE CANCER CARE. The Journal of Urology, 211(5S), e110.

Feng, J., Subbaswamy, A., Gossmann, A., Singh, H., Sahiner, B., Kim, M. O., … Pirracchio, R., & Xia, F. (2024, March). Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. In Causal Learning and Reasoning (pp. 587-608). PMLR.

Cho, C. J., Mohamed, A., Li, S. W., Black, A. W., & Anumanchipalli, G. K. (2024, April). SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in Hubert. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12076-12080). IEEE.

Mehandru, N., Miao, B. Y., Almaraz, E. R., Sushil, M., Butte, A. J., & Alaa, A. (2024). Evaluating large language models as agents in the clinic. NPJ Digital Medicine, 7(1), 84.

Miao, B. Y., Rodriguez Almaraz, E., Ashraf Ganjouei, A., Suresh, A., Zack, T., Bravo, M., … Alaa, A., & Butte, A. J. (2024). Generation of guideline-based clinical decision trees in oncology using large language models. medRxiv, 2024-03.

Friesner, I. D., Feng, J., Kalnicki, S., Garg, M., Ohri, N., & Hong, J. C. (2024). Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts. JAMA oncology, e240014. Advance online publication. https://doi.org/10.1001/jamaoncol.2024.0014

Adler-Milstein, J., Redelmeier, D. A., & Wachter, R. M. (2024). The Limits of Clinician Vigilance as an AI Safety Bulwark. JAMA, 10.1001/jama.2024.3620. Advance online publication. https://doi.org/10.1001/jama.2024.3620.

Balzer, L. B., Cai, E., Godoy Garraza, L., & Amaranath, P. (2024). Adaptive selection of the optimal strategy to improve precision and power in randomized trials. Biometrics, 80(1), ujad034. https://doi.org/10.1093/biomtc/ujad034

Natesan, D., Eisenstein, E. L., Thomas, S. M., Eclov, N. C., Dalal, N. H., Stephens, S. J., … & Hong, J. C. (2024). Health Care Cost Reductions with Machine Learning–Directed Evaluations during Radiation Therapy—An Economic Analysis of a Randomized Controlled Study. NEJM AI, AIoa2300118.

Ge, J., Buenaventura, A., Berrean, B., Purvis, J., Fontil, V., Lai, J. C., & Pletcher, M. J. (2024). Applying human-centered design to the construction of a cirrhosis management clinical decision support system. Hepatology communications, 8(3), e0394. https://doi.org/10.1097/HC9.0000000000000394.

Goldberg, C. B., Adams, L., Blumenthal, D., Brennan, P. F., Brown, N., Butte, A. J., … & Kohane, I. S. (2024). To do no harm—and the most good—with AI in health care. NEJM AI, 1(3), AIp2400036.

Miao, B. Y., Sushil, M., Xu, A., Wang, M., Arneson, D., Berkley, E., … & Butte, A. J. (2024). Characterisation of digital therapeutic clinical trials: a systematic review with natural language processing. The Lancet Digital Health, 6(3), e222-e229.

Sushil, M., Kennedy, V. E., Mandair, D., Miao, B. Y., Zack, T., & Butte, A. J. (2024). CORAL: Expert-Curated Oncology Reports to Advance Language Model Inference. NEJM AI, AIdbp2300110.

Natesan, D., Eisenstein, E. L., Thomas, S. M., Eclov, N. C., Dalal, N. H., Stephens, S. J., … & Hong, J. C. (2024). Health Care Cost Reductions with Machine Learning–Directed Evaluations during Radiation Therapy—An Economic Analysis of a Randomized Controlled Study. NEJM AI, AIoa2300118.

Miao, B. Y., Sushil, M., Xu, A., Wang, M., Arneson, D., Berkley, E., … Rudrapatna, V., &  Butte, A. J. (2024). Characterisation of digital therapeutic clinical trials: a systematic review with natural language processing. The Lancet Digital Health, 6(3), e222-e229.

Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., … Baranzini, S., & Sirota, M. (2024). Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights. Nature Aging, 1-17.

Lin H, Ni L., Phuong, C., Hong, JC. Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways. Pharmgenomics Pers Med. 2024;17:65-76. https://doi.org/10.2147/PGPM.S396971

Nong, P., Adler-Milstein, J., Kardia, S., & Platt, J. (2024). Public perspectives on the use of different data types for prediction in healthcare. Journal of the American Medical Informatics Association, ocae009.

Garcia-Alamino, J. M., & Pirracchio, R. (2024). Harnessing machine learning for the early prediction of ventilator-associated pneumonia: A leap towards precision in critical care. European journal of internal medicine, S0953-6205(24)00018-9. Advance online publication. https://doi.org/10.1016/j.ejim.2024.01.011.

Hédou, J., Marić, I., Bellan, G., Einhaus, J., Gaudillière, D. K., Ladant, F. X., … Sirota, M., & Gaudillière, B. (2024). Discovery of sparse, reliable omic biomarkers with Stabl. Nature Biotechnology, 1-13.

Arvisais-Anhalt, S., Gonias, S. L., & Murray, S. G. (2024). Establishing priorities for implementation of large language models in pathology and laboratory medicine. Academic Pathology, 11(1). 

Pearson, T. A., Vitalis, D., Pratt, C., Campo, R., Armoundas, A. A., Au, D., Beech, B., Brazhnik, O., Chute, C. G., Davidson, K. W., Diez-Roux, A. V., Fine, L. J., Gabriel, D., Groenveld P., Hall, J., Hamilton, A. B., Hu, H., Ji, H., Kind, A., Kraus, W. E., Murray, D. M., Neumark-Sztainer, D., Petersen, M., Goff, D. (2024). The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities. JACC: Advances, 3(1), 100759.

Fong, N., Feng, J., Hubbard, A., Dang, L. E., & Pirracchio, R. (2024). IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study. Critical Care Explorations, 6(1), e1024.

Zack, T., Lehman, E., Suzgun, M., Rodriguez, J. A., Celi, L. A., Gichoya, J., … Butte, A., & Alsentzer, E. (2024). Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. The Lancet Digital Health, 6(1), e12-e22.

Rule, A., Kannampallil, T., Hribar, M. R., Dziorny, A. C., Thombley, R., Apathy, N. C., Adler-Milstein, J. (2023). Guidance for reporting analyses of metadata on electronic health record use. Journal of the American Medical Informatics Association, ocad254.

McCoy, D., Schuler, A., Hubbard A., van der Laan M (2023). SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R. Journal of Open Source Software, 8(91), 5422, https://doi.org/10.21105/joss.05422.

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.