An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

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.

An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques

“…The simulations showed that the proposed framework was highly efficient for diagnosing and predicting life-threatening diseases. Fang et al [19] presented a medical information fusion-based diagnostic framework for the treatment of COVID-19 disease. The proposed methodology could improve classification accuracy more than the traditional methods and help healthcare professionals combat COVID-19.…”

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

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Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States

“…The complex network theory has been proved to provide a unique opportunity to objectively uncover the inner topology relationships of multi-dimensional data from a global view ( Baggio et al., 2021 ; Qiao et al., 2019 ). Hu et al. (2021) built a heterogeneous information network to uncover the complicated relationships in the syndromes, symptoms, and medicines, aiming to support the COVID-19 diagnosis and treatment analysis.…”