no code implementations • BioNLP (ACL) 2022 • Matúš Falis, Hang Dong, Alexandra Birch, Beatrice Alex
We propose data augmentation and synthesis techniques in order to address these scenarios.
1 code implementation • 24 Jan 2024 • Matúš Falis, Aryo Pradipta Gema, Hang Dong, Luke Daines, Siddharth Basetti, Michael Holder, Rose S Penfold, Alexandra Birch, Beatrice Alex
Neural coding models were trained on baseline and augmented data and evaluated on a MIMIC-IV test set.
1 code implementation • 21 Mar 2022 • Hang Dong, Matúš Falis, William Whiteley, Beatrice Alex, Joshua Matterson, Shaoxiong Ji, Jiaoyan Chen, Honghan Wu
Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.
1 code implementation • EMNLP 2021 • Matúš Falis, Hang Dong, Alexandra Birch, Beatrice Alex
We propose a set of metrics for hierarchical evaluation using the depth-based representation.
Multi Label Text Classification Multi-Label Text Classification +1
no code implementations • 31 Jul 2020 • Patrick Schrempf, Hannah Watson, Shadia Mikhael, Maciej Pajak, Matúš Falis, Aneta Lisowska, Keith W. Muir, David Harris-Birtill, Alison Q. O'Neil
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain.
no code implementations • 10 Oct 2019 • Mattias Appelgren, Patrick Schrempf, Matúš Falis, Satoshi Ikeda, Alison Q. O'Neil
However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages.