1 code implementation • 17 Mar 2024 • Kung Yin Hong, Lifeng Han, Riza Batista-Navarro, Goran Nenadic
We present the models we fine-tuned using the limited amount of real data and the synthetic data we generated using back-translation including OpusMT, NLLB, and mBART.
1 code implementation • 12 Dec 2023 • Lifeng Han, Serge Gladkoff, Gleb Erofeev, Irina Sorokina, Betty Galiano, Goran Nenadic
Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs).
no code implementations • 17 Nov 2023 • Warren Del-Pinto, George Demetriou, Meghna Jani, Rikesh Patel, Leanne Gray, Alex Bulcock, Niels Peek, Andrew S. Kanter, William G Dixon, Goran Nenadic
A gold standard was constructed by a panel of clinicians from a subset of the annotated diagnoses.
1 code implementation • 30 Oct 2023 • Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Warren Del-Pinto, Goran Nenadic
LT3 is trained on a set of around 2K lines of medication prescriptions extracted from the MIMIC-III database, allowing the model to produce valuable synthetic medication prescriptions.
no code implementations • 3 Oct 2023 • Hangyu Tu, Lifeng Han, Goran Nenadic
Furthermore, we also designed a set of post-processing roles to generate structured output on medications and the temporal relation.
1 code implementation • 22 Sep 2023 • Zihao Li, Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Matthew Shardlow, Goran Nenadic
In this work, we investigate the ability of state-of-the-art large language models (LLMs) on the task of biomedical abstract simplification, using the publicly available dataset for plain language adaptation of biomedical abstracts (\textbf{PLABA}).
1 code implementation • 12 Aug 2023 • Jie Yang, Soyeon Caren Han, Siqu Long, Josiah Poon, Goran Nenadic
Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives.
1 code implementation • 7 Aug 2023 • Haifa Alrdahi, Lifeng Han, Hendrik Šuvalov, Goran Nenadic
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 language models (LMs).
no code implementations • 31 Jul 2023 • Serge Gladkoff, Gleb Erofeev, Irina Sorokina, Lifeng Han, Goran Nenadic
Translation Quality Evaluation (TQE) is an essential step of the modern translation production process.
1 code implementation • 5 Jul 2023 • Viktor Schlegel, Hao Li, Yuping Wu, Anand Subramanian, Thanh-Tung Nguyen, Abhinav Ramesh Kashyap, Daniel Beck, Xiaojun Zeng, Riza Theresa Batista-Navarro, Stefan Winkler, Goran Nenadic
This paper describes PULSAR, our system submission at the ImageClef 2023 MediQA-Sum task on summarising patient-doctor dialogues into clinical records.
1 code implementation • 5 Jun 2023 • Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Thanh-Tung Nguyen, Abhinav Ramesh Kashyap, Xiaojun Zeng, Daniel Beck, Stefan Winkler, Goran Nenadic
Medical progress notes play a crucial role in documenting a patient's hospital journey, including his or her condition, treatment plan, and any updates for healthcare providers.
no code implementations • 25 May 2023 • Hao Li, Viktor Schlegel, Riza Batista-Navarro, Goran Nenadic
Furthermore, evaluating key points is crucial in ensuring that the automatically generated summaries are useful.
no code implementations • 8 Mar 2023 • Serge Gladkoff, Lifeng Han, Goran Nenadic
Then, this leads to our example with two human-generated observational scores, for which, we introduce ``Student's \textit{t}-Distribution'' method and explain how to use it to measure the IRR score using only these two data points, as well as the confidence intervals (CIs) of the quality evaluation.
2 code implementations • 8 Jan 2023 • Bernadeta Griciūtė, Lifeng Han, Goran Nenadic
In this study, from the social-media and healthcare domain, we apply popular Latent Dirichlet Allocation (LDA) methods to model the topic changes in Swedish newspaper articles about Coronavirus.
2 code implementations • 23 Oct 2022 • Samuel Belkadi, Lifeng Han, Yuping Wu, Goran Nenadic
The experimental outcomes show that 1) CRF layers improved all language models; 2) referring to BIO-strict span level evaluation using macro-average F1 score, although the fine-tuned LLMs achieved 0. 83+ scores, the TransformerCRF model trained from scratch achieved 0. 78+, demonstrating comparable performances with much lower cost - e. g. with 39. 80\% less training parameters; 3) referring to BIO-strict span-level evaluation using weighted-average F1 score, ClinicalBERT-CRF, BERT-CRF, and TransformerCRF exhibited lower score differences, with 97. 59\%/97. 44\%/96. 84\% respectively.
no code implementations • 12 Oct 2022 • Lifeng Han, Gleb Erofeev, Irina Sorokina, Serge Gladkoff, Goran Nenadic
To the best of our knowledge, this is the first work on using MMPLMs towards \textit{clinical domain transfer-learning NMT} successfully for totally unseen languages during pre-training.
1 code implementation • 8 Oct 2022 • Yuping Wu, Ching-Hsun Tseng, Jiayu Shang, Shengzhong Mao, Goran Nenadic, Xiao-jun Zeng
To fill these gaps, this paper first conducts the comparison analysis of oracle summaries based on EDUs and sentences, which provides evidence from both theoretical and experimental perspectives to justify and quantify that EDUs make summaries with higher automatic evaluation scores than sentences.
no code implementations • 15 Sep 2022 • Lifeng Han, Gleb Erofeev, Irina Sorokina, Serge Gladkoff, Goran Nenadic
Pre-trained language models (PLMs) often take advantage of the monolingual and multilingual dataset that is freely available online to acquire general or mixed domain knowledge before deployment into specific tasks.
1 code implementation • 7 Dec 2020 • Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro
Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans.
no code implementations • EMNLP (Louhi) 2020 • Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, Hao Ni, Goran Nenadic, Alejo Nevado-Holgado
In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).
no code implementations • 29 May 2020 • Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro
Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data.
1 code implementation • 24 May 2020 • Nikola Milosevic, Gangamma Kalappa, Hesam Dadafarin, Mahmoud Azimaee, Goran Nenadic
The software is able to perform named entity recognition using some of the state-of-the-art techniques and then mask or redact recognized entities.
1 code implementation • LREC 2020 • Viktor Schlegel, Marco Valentino, André Freitas, Goran Nenadic, Riza Batista-Navarro
Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text.
1 code implementation • 23 Sep 2019 • Maksim Belousov, Nikola Milosevic, Ghada Alfattni, Haifa Alrdahi, Goran Nenadic
The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%.
1 code implementation • WS 2019 • Maksim Belousov, William G. Dixon, Goran Nenadic
The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA.
no code implementations • 28 May 2019 • Maksim Belousov, Nikola Milosevic, William Dixon, Goran Nenadic
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced after the administration of a certain drug or a combination of drugs, presenting a challenge for drug development and drug administration.
no code implementations • 22 May 2019 • Nikola Milosevic, Dimitar Marinov, Abdullah Gok, Goran Nenadic
In the past decade, social innovation projects have gained the attention of policy makers, as they address important social issues in an innovative manner.
1 code implementation • 26 Feb 2019 • Nikola Milosevic, Cassie Gregson, Robert Hernandez, Goran Nenadic
The scientific literature is growing exponentially, and professionals are no more able to cope with the current amount of publications.
no code implementations • 22 Nov 2018 • Nikola Milosevic, Goran Nenadic
Simile is a figure of speech that compares two things through the use of connection words, but where comparison is not intended to be taken literally.
1 code implementation • 20 May 2016 • Nikola Milosevic, Goran Nenadic
Similes are natural language expressions used to compare unlikely things, where the comparison is not taken literally.
no code implementations • SEMEVAL 2013 • Michele Filannino, Gavin Brown, Goran Nenadic
This paper describes a temporal expression identification and normalization system, ManTIME, developed for the TempEval-3 challenge.
no code implementations • BMC Bioinformatics 2010 • Martin Gerner, Goran Nenadic, Casey M Bergman
In this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions.