Position-based Prompting for Health Outcome Generation

Autor: Abaho, M., Bollegala, D., Williamson, P., Dodd, S.

Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been effective especially when these LMs are fine-tuned towards not just dat

Externí odkaz: http://arxiv.org/abs/2204.03489

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.

Autor: Abuzour AS; Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom.; Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom., Wilson SA; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Woodall AA; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom.; Directorate of Mental Health and Learning Disabilities, Powys Teaching Health Board, Bronllys, United Kingdom., Mair FS; General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom., Clegg A; Academic Unit for Ageing & Stroke Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Leeds, Bradford, United Kingdom.; Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom., Shantsila E; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Gabbay M; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Abaho M; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Aslam A; Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom.; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom., Bollegala D; Department of Computer Science, University of Liverpool, Liverpool, United Kingdom., Cant H; Division of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom., Griffiths A; NIHR Applied Research Collaboration North West Coast, United Kingdom., Hama L; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.; School of Computing, University of Leeds, Leeds, United Kingdom., Leeming G; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Lo E; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Maskell S; Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom., O'Connell M; Division of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom., Popoola O; Merseycare NHS Foundation Trust, Liverpool, United Kingdom., Relton S; Faculty of Medicine and Health, School of Medicine, University of Leeds, Leeds, United Kingdom.; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom., Ruddle RA; Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom.; School of Computing, University of Leeds, Leeds, United Kingdom., Schofield P; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Sperrin M; Division of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom., Staa TV; Division of Informatics, Imaging & Data Science, University of Manchester, Manchester, United Kingdom., Buchan I; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom., Walker LE; Centre for Experimental Therapeutics, University of Liverpool, Liverpool, United Kingdom.; Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom.

Publikováno v: PloS one [PLoS One] 2024 Aug 30; Vol. 19 (8), pp. e0299770. Date of Electronic Publication: 2024 Aug 30 (Print Publication: 2024).

The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.

Autor: Walker LE; Wolfson Centre for Personalized Medicine, University of Liverpool, Liverpool, UK., Abuzour AS; Academic Unit for Ageing & Stroke Research, University of Leeds, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK., Bollegala D; Department of Computer Science, University of Liverpool, UK., Clegg A; Academic Unit for Ageing & Stroke Research, University of Leeds, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK., Gabbay M; Institute of Population Health, University of Liverpool, Liverpool, UK., Griffiths A; Public Advisor., Kullu C; Mersey Care NHS Foundation Trust, Liverpool, UK., Leeming G; Civic Data Cooperative, University of Liverpool, Liverpool, UK., Mair FS; General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, UK., Maskell S; School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, UK., Relton S; Institute of Health Sciences, University of Leeds, UK., Ruddle RA; School of Computing and Leeds Institute for Data Analytics, University of Leeds, UK., Shantsila E; Institute of Population Health, University of Liverpool, Liverpool, UK., Sperrin M; Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK., Van Staa T; Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK., Woodall A; Directorate of Mental Health and Learning Disabilities, Powys Teaching Health Board, Bronllys, UK., Buchan I; Institute of Population Health, University of Liverpool, Liverpool, UK.

Publikováno v: Journal of multimorbidity and comorbidity [J Multimorb Comorb] 2022 Dec 15; Vol. 12, pp. 26335565221145493. Date of Electronic Publication: 2022 Dec 15 (Print Publication: 2022).

Tick parasitism classification from noisy medical records

Autor: Neill, JO, Bollegala, D, Radford, AD, Noble, PJ

Publikováno v: CEUR Workshop Proceedings

Much of the health information in the medical domain comes in the form of clinical narratives. The rich semantic information contained in these notes can be modeled to make inferences that assist the decision making process for medical practitioners,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::ff0cfbb6d8910c626c48eeb575725c7d
http://livrepository.liverpool.ac.uk/3047268/1/oneill_kdh_2019.pdf

Joint Learning of Sense and Word Embeddings

Autor: Mohammed Alsuhaibani, Bollegala, D.

Publikováno v: LREC
Scopus-Elsevier

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::acd7c48db466cd30667d52b715b5ab5e
http://livrepository.liverpool.ac.uk/3014889/1/SAWE.pdf