ArticleAn international journal on information fusion2021
An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion.
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 7 papers.
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Who cites it
7 citing papers in PubMed.
- A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis.BMC medical informatics and decision making · 2024Article
- Data-driven engineering framework with AI algorithm of Ginkgo Folium tablets manufacturing.Acta pharmaceutica Sinica. B · 2023Article
- An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation.Frontiers in neuroinformatics · 2023Article
- Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.PloS one · 2023Article
- A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.Computer methods and programs in biomedicine · 2022Article
- Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States.Sustainable cities and society · 2022Article
- Editorial: Advances in multi-source information fusion for epidemic diseases.An international journal on information fusion · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.
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