[HTML][HTML] Few-shot learning for medical text: A review of advances, trends, and opportunities

Y Ge, Y Guo, S Das, MA Al-Garadi, A Sarker - Journal of Biomedical …, 2023 - Elsevier
Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

Event detection in online social network: Methodologies, state-of-art, and evolution

X Hu, W Ma, C Chen, S Wen, J Zhang, Y Xiang… - Computer Science …, 2022 - Elsevier
Online social network such as Twitter, Facebook and Instagram are increasingly becoming
the go-to medium for users to acquire information and discuss what is happening globally …

Communication-efficient federated learning via knowledge distillation

C Wu, F Wu, L Lyu, Y Huang, X Xie - Nature communications, 2022 - nature.com
Federated learning is a privacy-preserving machine learning technique to train intelligent
models from decentralized data, which enables exploiting private data by communicating …

No one left behind: Inclusive federated learning over heterogeneous devices

R Liu, F Wu, C Wu, Y Wang, L Lyu, H Chen… - Proceedings of the 28th …, 2022 - dl.acm.org
Federated learning (FL) is an important paradigm for training global models from
decentralized data in a privacy-preserving way. Existing FL methods usually assume the …

Transformers in healthcare: A survey

S Nerella, S Bandyopadhyay, J Zhang… - arXiv preprint arXiv …, 2023 - arxiv.org
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

[HTML][HTML] Emotions and topics expressed on Twitter during the COVID-19 pandemic in the United Kingdom: comparative geolocation and text mining analysis

H Alhuzali, T Zhang, S Ananiadou - Journal of Medical Internet Research, 2022 - jmir.org
Background In recent years, the COVID-19 pandemic has brought great changes to public
health, society, and the economy. Social media provide a platform for people to discuss …

[HTML][HTML] Assessing the performance of clinical natural language processing systems: development of an evaluation methodology

L Canales, S Menke, S Marchesseau… - JMIR Medical …, 2021 - medinform.jmir.org
Background: Clinical natural language processing (cNLP) systems are of crucial importance
due to their increasing capability in extracting clinically important information from free text …

Artificial intelligence: revolutionizing cardiology with large language models

MJ Boonstra, D Weissenbacher, JH Moore… - European Heart …, 2024 - academic.oup.com
Natural language processing techniques are having an increasing impact on clinical care
from patient, clinician, administrator, and research perspective. Among others are automated …

DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter

A Magge, E Tutubalina, Z Miftahutdinov… - Journal of the …, 2021 - academic.oup.com
Objective Research on pharmacovigilance from social media data has focused on mining
adverse drug events (ADEs) using annotated datasets, with publications generally focusing …

Mining social media data for biomedical signals and health-related behavior

RB Correia, IB Wood, J Bollen… - Annual review of …, 2020 - annualreviews.org
Social media data have been increasingly used to study biomedical and health-related
phenomena. From cohort-level discussions of a condition to population-level analyses of …