1 code implementation • SemEval (NAACL) 2022 • Mohammadmahdi Nouriborji, Omid Rohanian, David Clifton
This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subtasks A and B.
1 code implementation • 19 Mar 2021 • Dani Kiyasseh, Tingting Zhu, David Clifton
Cardiac signals, such as the electrocardiogram, convey a significant amount of information about the health status of a patient which is typically summarized by a clinician in the form of a clinical report, a cumbersome process that is prone to errors.
1 code implementation • 17 Oct 2022 • Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji, Vinod Kumar Chauhan, Bronner P. Gonçalves, Christiana Kartsonaki, ISARIC Clinical Characterisation Group, Laura Merson, David Clifton
Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP.
no code implementations • ICML 2020 • Rasheed el-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, David Clifton
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department.
no code implementations • 9 Jan 2022 • Omid Rohanian, Samaneh Kouchaki, Andrew Soltan, Jenny Yang, Morteza Rohanian, Yang Yang, David Clifton
One of our main contributions is that we specifically target the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks.
no code implementations • 31 Mar 2022 • Anna Antoniou, Giacomo Dossena, Julia MacMillan, Steven Hamblin, David Clifton, Paula Petrone
The objective of this work is to calculate the risk of re-identification arising from a malicious attack to an anonymised dataset, as described below.
no code implementations • 5 May 2023 • Alex Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David Clifton, Nigam H. Shah
We submit that external validation is insufficient to establish ML models' safety or utility.
no code implementations • 16 Feb 2024 • Niall Taylor, Upamanyu Ghose, Omid Rohanian, Mohammadmahdi Nouriborji, Andrey Kormilitzin, David Clifton, Alejo Nevado-Holgado
The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods.
no code implementations • 2 Apr 2024 • James Anibal, Hannah Huth, Ming Li, Lindsey Hazen, Yen Minh Lam, Nguyen Thi Thu Hang, Michael Kleinman, Shelley Ost, Christopher Jackson, Laura Sprabery, Cheran Elangovan, Balaji Krishnaiah, Lee Akst, Ioan Lina, Iqbal Elyazar, Lenny Ekwati, Stefan Jansen, Richard Nduwayezu, Charisse Garcia, Jeffrey Plum, Jacqueline Brenner, Miranda Song, Emily Ricotta, David Clifton, C. Louise Thwaites, Yael Bensoussan, Bradford Wood
This report introduces a consortium of partners for global work, presents the application used for data collection, and showcases the potential of informative voice EHR to advance the scalability and diversity of audio AI.