Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

Ashique, Sumel; Mishra, Neeraj; Mohanto, Sourav; Garg, Ashish; Taghizadeh-Hesary, Farzad; Gowda, B.H. Jaswanth; Chellappan, Dinesh Kumar

doi:10.1016/j.heliyon.2024.e25754

Cited by 3 publications

(2 citation statements)

References 124 publications

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“…This analysis enables targeted interventions, diagnosis, and preventive measures to reduce human-to-human infectivity of COVID-19. The employment of AI has also shown great potential in advancing COVID-19 vaccine development by predicting potential epitopes with antigenic characteristics and detecting virus mutations through deep convolutional neural networks (CNN) [6,7]. Similarly, applying deep learning approaches has yielded promising results in identifying Acute Lymphoblastic Leukemia (ALL).…”

Section: Introductionmentioning

confidence: 99%

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Messai,

Drif,

Ouyahia

et al. 2024

Mach. Learn.: Sci. Technol.

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Meningitis, characterized by meninges and cerebrospinal fluid (CSF) inflammation, poses diagnostic challenges due to diverse clinical manifestations. This work introduces an explainable AI automatic medical decision methodology that determines critical features and their relevant values for the differential diagnosis of various meningitis cases. We proceed with knowledge acquisition to define the rules for this research. Currently, we have established the etiological diagnosis of Meningococcaemia, Meningococcal Meningitis, Tuberculous Meningitis, Aseptic Meningitis, Haemophilus influenzae Meningitis, and PneumococcalMeningitis. The data preprocessing was conducted after collecting data from samples with meningitis diseases at Setif Hospital in Algeria. Tree-based ensemble methods were then applied to assess the model’s performance. Finally, we implement an XAI agnostic explainability approach based on the SHapley Additive exPlanations technique to attribute each feature’s contribution to the model’s output. Experiments were conducted on the collected dataset and the SINAN database, obtained from the Brazilian Government’s Health Information System on Notifiable Diseases, which comprises 6729 patients aged over 18 years. The Extreme Gradient Boosting model was chosen for its superior performance metrics (Accuracy: 0.90, AUROC: 0.94, and F1-score: 0.98). Setif’s hospital data revealed notable performance metrics (Accuracy: 0.7143, F1-Score: 0.7857). This study's findings showcase each feature's contribution to the model’s predictions and diagnosis. It also reveals critical biomarker ranges associated with distinct types of Meningitis. Significant diagnostic effect was found for Meningococcal Meningitis with elevated neutrophil levels (>40%) and balanced lymphocyte levels (40-60%). Tuberculous Meningitis demonstrated low neutrophil levels (<60%) and elevated lymphocyte levels (>60%). Haemophilus influenzae meningitis exhibited a predominance of neutrophils (>80%), while Aseptic meningitis showed lower neutrophil levels (<40%) and lymphocyte levels within the range of 50-60%. The majority of the AI automatic medical decision results are twinned with validation by our team of infectious disease experts, confirming the alignment of algorithmic diagnoses with clinical practices.

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Section: Introductionmentioning

confidence: 99%

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Messai,

Drif,

Ouyahia

et al. 2024

Mach. Learn.: Sci. Technol.

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“…With the emergence of advanced technologies, particularly in artificial intelligence (AI) and ML, a growing opportunity exists to harness the available data for predictive modeling and disease management. Previous studies have demonstrated the efficacy of these technologies in diagnosing and managing viral diseases, including COVID-19 [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data

Sethi,

Shakyawar,

Reddy

et al. 2024

Diagnostics

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The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19 severity. With the varied clinical outcomes observed among infected individuals, creating a reliable machine learning (ML) model for predicting the severity of COVID-19 became paramount. Despite the availability of large-scale genomic and clinical data, previous studies have not effectively utilized multi-modality data for disease severity prediction using data-driven approaches. Our primary goal is to predict COVID-19 severity using a machine-learning model trained on a combination of patients’ gene expression, clinical features, and co-morbidity data. Employing various ML algorithms, including Logistic Regression (LR), XGBoost (XG), Naïve Bayes (NB), and Support Vector Machine (SVM), alongside feature selection methods, we sought to identify the best-performing model for disease severity prediction. The results highlighted XG as the superior classifier, with 95% accuracy and a 0.99 AUC (Area Under the Curve), for distinguishing severity groups. Additionally, the SHAP analysis revealed vital features contributing to prediction, including several genes such as COX14, LAMB2, DOLK, SDCBP2, RHBDL1, and IER3-AS1. Notably, two clinical features, the absolute neutrophil count and Viremia Categories, emerged as top contributors. Integrating multiple data modalities has significantly improved the accuracy of disease severity prediction compared to using any single modality. The identified features could serve as biomarkers for COVID-19 prognosis and patient care, allowing clinicians to optimize treatment strategies and refine clinical decision-making processes for enhanced patient outcomes.

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Applications of reinforcement learning, machine learning, and virtual screening in SARS-CoV-2-related proteins

Matsuzaka,

Yashiro

2024

Comput. Artif. Intell.

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Similarly, to all coronaviruses, SARS-CoV-2 uses the S glycoprotein to enter host cells, which contains two functional domains: S1 and S2 receptor binding domain (RBD). Angiotensin-converting enzyme 2 (ACE2) is recognizable by the S proteins on the surface of the SARS-CoV-2 virus. The SARS-CoV-2 virus causes SARS, but some mutations in the RBD of the S protein markedly enhance their binding affinity to ACE2. Searching for new compounds in COVID-19 is an important initial step in drug discovery and materials design. Still, the problem is that this search requires trial-and-error experiments, which are costly and time-consuming. In the automatic molecular design method based on deep reinforcement learning, it is possible to design molecules with optimized physical properties by combining a newly devised coarse-grained representation of molecules with deep reinforcement learning. Also, structured-based virtual screening uses protein 3D structure information to evaluate the binding affinity between proteins and compounds based on physicochemical interactions such as van der Waals forces, Coulomb forces, and hydrogen bonds, and select drug candidate compounds. In addition, AlphaFold can predict 3D protein structures, given the amino acid sequence, and the protein building blocks. Ensemble docking, in which multiple protein structures are generated using the molecular dynamics method and docking calculations are performed for each, is often performed independently of docking calculations. In the future, the AlphaFold algorithm can be used to predict various protein structures related to COVID-19.

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Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

Cited by 3 publications

References 124 publications

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data

Applications of reinforcement learning, machine learning, and virtual screening in SARS-CoV-2-related proteins

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