ArticleAn international journal on information fusion2021
Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis.
Article in An international journal on information fusion, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 61 citations in OpenAlex.
- Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Enhancing ophthalmology medical record management with multi-modal knowledge graphs.Scientific reports · 2024Article
- Music recommendation algorithms based on knowledge graph and multi-task feature learning.Scientific reports · 2024Article
- Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications.PeerJ. Computer science · 2024Article
- A scoping review on multimodal deep learning in biomedical images and texts.Journal of biomedical informatics · 2023Article
- Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study.JMIR medical informatics · 2022Article
- Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography.Journal of imaging · 2022Article
- Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic.Journal of imaging · 2022Review
- Knowledge-Based Recurrent Neural Network for TCM Cerebral Palsy Diagnosis.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review.Health data science · 2022Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. In general, there are two issues to overcome: (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information should be taken into account together to make accurate inferences. To address these challenges, we propose a multi-modal knowledge graph attention embedding for COVID-19 diagnosis. Our method not only learns the relational embedding from nodes in a constituted knowledge graph but also has access to medical knowledge, aiming at improving the performance of the classifier through the mechanism of medical knowledge attention. The experimental results show that our approach significantly improves classification performance compared to other state-of-the-art techniques and possesses robustness for each modality from multi-modal data. Moreover, we construct a new COVID-19 multi-modal dataset based on text mining, consisting of 1393 doctor-patient dialogues and their 3706 images (347 X-ray
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.