6 citations · 22 across the 17 of their papers we have counts for
18 papers
MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement -- LLaMA3.1 and GPT-4o for Complete Abstract Adaptation
Zhidong Ling, Zihao Li, Pablo Romero +2
This report is the system description of the MaLei team (Manchester and Leiden) for the shared task Plain Language Adaptation of Biomedical Abstracts (PLABA) 2024 (we had an earlie…
DeIDClinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment
Angel Paul, Dhivin Shaji, Lifeng Han +3
The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream…
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…
Synthetic4Health: Generating Annotated Synthetic Clinical Letters
Libo Ren, Samuel Belkadi, Lifeng Han +2
Since clinical letters contain sensitive information, clinical-related datasets can not be widely applied in model training, medical research, and teaching. This work aims to gener…
INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning
Pablo Romero, Lifeng Han, Goran Nenadic
Medication Extraction and Mining play an important role in healthcare NLP research due to its practical applications in hospital settings, such as their mapping into standard clini…
AutoLLM-CARD: Towards a Description and Landscape of Large Language Models
Shengwei Tian, Lifeng Han, Goran Nenadic
With the rapid growth of the Natural Language Processing (NLP) field, a vast variety of Large Language Models (LLMs) continue to emerge for diverse NLP tasks. As more papers are pu…
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…
A Comparative Study on Automatic Coding of Medical Letters with Explainability
Jamie Glen, Lifeng Han, Paul Rayson +1
This study aims to explore the implementation of Natural Language Processing (NLP) and machine learning (ML) techniques to automate the coding of medical letters with visualised ex…
CANTONMT: Investigating Back-Translation and Model-Switch Mechanisms for Cantonese-English Neural Machine Translation
Kung Yin Hong, Lifeng Han, Riza Batista-Navarro +1
This paper investigates the development and evaluation of machine translation models from Cantonese to English, where we propose a novel approach to tackle low-resource language tr…
The Multi-Range Theory of Translation Quality Measurement: MQM scoring models and Statistical Quality Control
Arle Lommel, Serge Gladkoff, Alan Melby +10
The year 2024 marks the 10th anniversary of the Multidimensional Quality Metrics (MQM) framework for analytic translation quality evaluation. The MQM error typology has been widely…
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…
CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation Data
Kung Yin Hong, Lifeng Han, Riza Batista-Navarro +1
Neural Machine Translation (NMT) for low-resource languages is still a challenging task in front of NLP researchers. In this work, we deploy a standard data augmentation methodolog…
Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning
Lifeng Han, Serge Gladkoff, Gleb Erofeev +3
We conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning such as Transformer based structures. Furthermore…
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…
Extraction of Medication and Temporal Relation from Clinical Text using Neural Language Models
Hangyu Tu, Lifeng Han, Goran Nenadic
Clinical texts, represented in electronic medical records (EMRs), contain rich medical information and are essential for disease prediction, personalised information recommendation…
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…
MedMine: Examining Pre-trained Language Models on Medication Mining
Haifa Alrdahi, Lifeng Han, Hendrik Šuvalov +1
Automatic medication mining from clinical and biomedical text has become a popular topic due to its real impact on healthcare applications and the recent development of powerful la…
MTUncertainty: Assessing the Need for Post-editing of Machine Translation Outputs by Fine-tuning OpenAI LLMs
Serge Gladkoff, Lifeng Han, Gleb Erofeev +2
Translation Quality Evaluation (TQE) is an essential step of the modern translation production process. TQE is critical in assessing both machine translation (MT) and human transla…