3 citations · 5 across the 9 of their papers we have counts for
10 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…
A pipeline for enabling path-specific causal fairness in observational health data
Aparajita Kashyap, Sara Matijevic, Noémie Elhadad +2
When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or exacerbate existing healthcare…
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…
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…
CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records
Chao Pang, Jiheum Park, Xinzhuo Jiang +5
Electronic Health Records (EHRs) provide a rich, longitudinal view of patient health and hold significant potential for advancing clinical decision support, risk prediction, and da…
Path-specific effects for pulse-oximetry guided decisions in critical care
Kevin Zhang, Yonghan Jung, Divyat Mahajan +2
Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate p…
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…
Explainable AI: Definition and attributes of a good explanation for health AI
Evangelia Kyrimi, Scott McLachlan, Jared M Wohlgemut +4
Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity…