Published in NeurIPS, 2026
Recommended citation: Kobayashi, Y., Ramesh, P., Chaudhry, M. A., Jeanselme, V., Gichoya, J., Koyejo, S., Capaccione, K., and Joshi, S. Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation. https://arxiv.org/pdf/2602.00541
Published in NeurIPS, 2026
Recommended citation: Jing, Z., Jeanselme, V., Kobayashi, Y., Lee, S. A., Pang, C., Kashyap, A., Li, Y., Jiang, X., and Joshi, S. One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models. https://arxiv.org/pdf/2602.00541
Published in JAMA Network Open, 2026
Recommended citation: Jeanselme, V., Austin, C., Han, J., Lewis, R. A., Hruby, G. W., Natarajan, K., and Joshi, S. AI-Generated Draft Response Tool, Message Turnaround Time, and After-Hours Replies Among Health Care Professionals. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2856029
Published in ICLR, 2026
Recommended citation: Choi*, Y.,Jeanselme*, V., and Joshi, S. ICYM2I: The illusion of multimodal informativeness under missingnes. https://arxiv.org/abs/2505.16953
Published in BMC Medical Research Methodology, 2026
Recommended citation: Jeanselme, V., Palma, M., and Barrett, J. Assessing the impact of variance heterogeneity and mis-specification in mixed-effects location-scale models. https://arxiv.org/abs/2505.18038
Published in Machine Learning for Health (ML4H), 2025
Recommended citation: Jeanselme, V., Yoon, C., Falck, F., Tom, B., and Barrett, J. Identifying treatment response subgroups in observational time-to-event data. In Machine Learning for Health. https://www.arxiv.org/abs/2408.03463
Published in Management Science, 2025
Recommended citation: De-Arteaga, M., Jeanselme, V., Dubrawski, A., Chouldechova, A. Leveraging Expert Consistency to Improve Algorithmic Decision Support. In Management Science. https://pubsonline.informs.org/doi/full/10.1287/mnsc.2022.01576
Published in Journal of the American Academy of Dermatology, 2025
Recommended citation: Jambusaria-Pahlajani, A.*, Jeanselme, V.*, Wang, D., Ran, N., Granger, E., Cañueto, J., Brodland, D., Carr, D., Carter, J., Carucci, J., Hirotsu, K., Karn, E., Koyfman, S., Mangold, A., Girardi, F., Shahwan, K., Srivastava, D., Vidimos, A., Willenbrink, T., Wysong, A., Lotter, W., Ruiz, E. (2025). riSCC: A personalized risk model for the development of poor outcomes in cutaneous squamous cell carcinoma. In Journal of the American Academy of Dermatology. https://www.jaad.org/article/S0190-9622(25)00373-1/abstract
Published in Machine Learning for Healthcare 2025, 2025
Recommended citation: Ketenci, M., Jeanselme, V., Nieva, H., Joshi, S., and Elhadad, N. ADHAM: Additive deep hazard analysis mixtures for interpretable survival regression. https://arxiv.org/pdf/2509.07108
Published in AAAI 2024 Spring Symposium on Clinical Foundation Models, 2024
Recommended citation: Jeanselme, V., Agarwal, N. and Wang, C. (2024, May). Review of Language Models for Survival Analysis. In AAAI 2024 Spring Symposium on Clinical Foundation Models. https://openreview.net/pdf?id=ZLUsZ52ibx
Published in Conference on Health, Inference, and Learning (CHIL), 2023
Recommended citation: Jeanselme, V., Yoon, C. H., Tom, B., Barrett, J. (2023, June). Neural Fine-Gray: Monotonic neural networks for competing risks. In Conference on Health, Inference, and Learning (pp. 379-392). PMLR. https://arxiv.org/abs/2305.06703
Published in NeurIPS Workshop TS4H, 2022
Recommended citation: Jeanselme, V., Martin, G., Peek, N., Sperrin, M., Tom, B., Barrett, J. (2022). DeepJoint: Robust Survival Modelling Under Clinical Presence Shift . In NeurIPS 2022 Workshop on Learning from Time Series for Health. https://arxiv.org/abs/2205.13481
Published in Machine Learning for Health (ML4H), 2022
Recommended citation: Jeanselme, V., De-Arteaga, M., Zhang, Z., Barrett, J., Tom, B, (2022, November). Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. In Machine Learning for Health (pp. 12-34). PMLR. https://proceedings.mlr.press/v193/jeanselme22a/jeanselme22a.pdf
Published in Springer - Advances in Data Analysis and Classification, 2022
Recommended citation: Boecking, B., Jeanselme, V., Dubrawski, A (2022). Constrained clustering and multiple kernel learning without pairwise constraint relaxation. In Advances in Data Analysis and Classification, 1-16. https://doi.org/10.1007/s11634-022-00507-5
Published in Conference on Health, Inference, and Learning (CHIL), 2022
Recommended citation: Jeanselme, V., Tom, B., Barrett, J. (2022, April). Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering. In Conference on Health, Inference, and Learning (pp. 92-102). PMLR. https://proceedings.mlr.press/v174/jeanselme22a/jeanselme22a.pdf
Published in Resuscitation Plus, 2021
Recommended citation: Jeanselme, V., De-Arteaga, M., Elmer, J., Perman, S. M., Dubrawski, A. (2021). Sex differences in post cardiac arrest discharge locations. In Resuscitation plus, 8, 100185. https://www.sciencedirect.com/science/article/pii/S2666520421001107
Published in AAAI Spring Symposium on Survival Analysis, 2021
Recommended citation: Nagpal, C.*, Jeanselme, V.*, Dubrawski, A. (2021, May). Deep parametric time-to-event regression with time-varying covariates. In Survival Prediction-Algorithms, Challenges and Applications (pp. 184-193). PMLR. https://proceedings.mlr.press/v146/nagpal21a.html
Published in Critical Care, 2020
Recommended citation: Yoon, J. H.*, Jeanselme, V.*, Dubrawski, A., Hravnak, M., Pinsky, M. R., Clermont, G. (2020). Prediction of Hypotension Events with Physiologic Vital Sign Signatures in The Intensive Care Unit. In Critical Care, 24(1), 1-9. https://ccforum.biomedcentral.com/articles/10.1186/s13054-020-03379-3