2 citations · 4 across the 3 of their papers we have counts for
6 papers
Large Language Models for Biomedical Text Simplification: Promising But Not There Yet
Zihao Li, Samuel Belkadi, Nicolo Micheletti +3
In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. Th…
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling
Samuel Belkadi, Libo Ren, Nicolo Micheletti +2
The vast amount of available medical records has the potential to improve healthcare and biomedical research. However, privacy restrictions make these data accessible for internal…
Exploration of Masked and Causal Language Modelling for Text Generation
Nicolo Micheletti, Samuel Belkadi, Lifeng Han +1
Large Language Models (LLMs) have revolutionised the field of Natural Language Processing (NLP) and have achieved state-of-the-art performance in practically every task in this fie…
Generating Medical Prescriptions with Conditional Transformer
Samuel Belkadi, Nicolo Micheletti, Lifeng Han +2
Access to real-world medication prescriptions is essential for medical research and healthcare quality improvement. However, access to real medication prescriptions is often limite…
Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts
Zihao Li, Samuel Belkadi, Nicolo Micheletti +3
Biomedical literature often uses complex language and inaccessible professional terminologies. That is why simplification plays an important role in improving public health literac…
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data
Raffaele Marchesi, Nicolo Micheletti, Giuseppe Jurman +1
Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting t…