1 code implementation • 12 Dec 2022 • Leibo Liu, Oscar Perez-Concha, Anthony Nguyen, Vicki Bennett, Louisa Jorm
XR-Transformer, the new SOTA model in the general extreme multi-label text classification domain, and XR-LAT, a novel adaptation of the XR-Transformer model, were also trained on the MIMIC-III dataset.
Multi Label Text Classification Multi-Label Text Classification +1
no code implementations • 16 Oct 2022 • Hongjiang Chen, Yang Wang, Leibo Liu, Shaojun Wei, Shouyi Yin
Deep learning applications are being transferred from the cloud to edge with the rapid development of embedded computing systems.
no code implementations • 16 Oct 2022 • Hongjiang Chen, Yang Wang, Leibo Liu, Shaojun Wei, Shouyi Yin
Due to user privacy and regulatory restrictions, federate learning (FL) is proposed as a distributed learning framework for training deep neural networks (DNN) on decentralized data clients.
1 code implementation • 22 Apr 2022 • Leibo Liu, Oscar Perez-Concha, Anthony Nguyen, Vicki Bennett, Louisa Jorm
In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents.
no code implementations • 1 Jan 2021 • Leibo Liu, Oscar Perez-Concha, Anthony Nguyen, Vicki Bennett, Louisa Jorm
Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, individual identification number, phone/fax number; 2) Modelling: training six named entity recognition (NER) deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and 3) De-identification: removing PII from the hospital discharge summaries.
no code implementations • 11 Dec 2019 • Xi Chen, Shouyi Yin, Dandan song, Peng Ouyang, Leibo Liu, Shaojun Wei
Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices.