2 citations · 8 across the 15 of their papers we have counts for
16 papers
Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation
Yuta Kobayashi, Pradyun Ramesh, Muhammad Ahmed Chaudhry +5
Vision-Language Models (VLMs) for radiology report generation are typically trained on retrospective clinical reports, which suffer from omission noise: clinically present findings…
One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models
Zilin Jing, Vincent Jeanselme, Yuta Kobayashi +6
Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory v…
Aligning Probabilistic Beliefs under Informative Missingness: LLM Steerability in Clinical Reasoning
Yuta Kobayashi, Vincent Jeanselme, Shalmali Joshi
Large Language Models (LLMs) are increasingly deployed for clinical reasoning tasks, which inherently require eliciting calibrated probabilistic beliefs based on available evidence…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva +2
Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictiv…
Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture
Vincent Jeanselme, Glen Martin, Matthew Sperrin +3
Electronic health records arise from the complex interaction between patients and the healthcare system. This observation process of interactions, referred to as clinical presence,…
Competing Risks: Impact on Risk Estimation and Algorithmic Fairness
Vincent Jeanselme, Brian Tom, Jessica Barrett
Accurate time-to-event prediction is integral to decision-making, informing medical guidelines, hiring decisions, and resource allocation. Survival analysis, the quantitative frame…
Assessing the impact of variance heterogeneity and misspecification in mixed-effects location-scale models
Vincent Jeanselme, Marco Palma, Jessica K Barrett
Linear Mixed Model (LMM) is a common statistical approach to model the relation between exposure and outcome while capturing individual variability through random effects. However,…
ICYM2I: The illusion of multimodal informativeness under missingness
Young Sang Choi, Vincent Jeanselme, Pierre Elias +1
Multimodal learning is of continued interest in artificial intelligence-based applications, motivated by the potential information gain from combining different data modalities. Ho…
FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records
Vincent Jeanselme, Zilin Jing, Aparajita Kashyap +9
Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (i…
Identifying treatment response subgroups in observational time-to-event data
Vincent Jeanselme, Chang Ho Yoon, Fabian Falck +2
Identifying patient subgroups with different treatment responses is an important task to inform medical recommendations, guidelines, and the design of future clinical trials. Exist…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Neural Fine-Gray: Monotonic neural networks for competing risks
Vincent Jeanselme, Chang Ho Yoon, Brian Tom +1
Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest. Despite com…
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang +2
Machine learning risks reinforcing biases present in data and, as we argue in this work, in what is absent from data. In healthcare, societal and decision biases shape patterns in…
DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
Vincent Jeanselme, Glen Martin, Niels Peek +3
Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself co…
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performanc…