1 citations · 2 across the 5 of their papers we have counts for
5 papers
ReToP: Learning to Rewrite Electronic Health Records for Clinical Prediction
Jesus Lovon-Melgarejo, Jose G. Moreno, Christine Damase-Michel +1
Electronic Health Records (EHRs) provide crucial information for clinical decision-making. However, their high-dimensionality, heterogeneity, and sparsity make clinical prediction…
Jointly Generating and Attributing Answers using Logits of Document-Identifier Tokens
Lucas Albarede, Jose Moreno, Lynda Tamine +1
Despite their impressive performances, Large Language Models (LLMs) remain prone to hallucination, which critically undermines their trustworthiness. While most of the previous wor…
Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for Electronic Health Records
Jesus Lovon, Thouria Ben-Haddi, Jules Di Scala +2
The lack of standardized evaluation benchmarks in the medical domain for text inputs can be a barrier to widely adopting and leveraging the potential of natural language models for…
PatientDx: Merging Large Language Models for Protecting Data-Privacy in Healthcare
Jose G. Moreno, Jesus Lovon, M'Rick Robin-Charlet +2
Fine-tuning of Large Language Models (LLMs) has become the default practice for improving model performance on a given task. However, performance improvement comes at the cost of t…
Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval
Jesus Lovon, Martin Mouysset, Jo Oleiwan +3
Electronic Health Record (EHR) tables pose unique challenges among which is the presence of hidden contextual dependencies between medical features with a high level of data dimens…