AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus

Keyphrases

• Clinical Decision Support Systems 100%
• Deep Learning Methods 100%
• Multisystem Inflammatory Syndrome in children (MIS-C) 100%
• Endemic Typhus 100%
• Clinical Features 33%
• Machine Learning Models 33%
• Child Patients 33%
• Statistical Learning Models 33%
• Typhus 33%
• Statistical Analysis 16%
• Early Detection 16%
• Sensitivity Specificity 16%
• Acute Infection 16%
• Emergency Department 16%
• Scoring System 16%
• Kawasaki Disease 16%
• Validation Data 16%
• Performance Assessment 16%
• Computer System 16%
• Statistical Significance 16%
• Medical Team 16%
• Laboratory Features 16%
• COVID-19 Pandemic 16%
• Diagnostic Challenge 16%
• Favorable Prognosis 16%
• Uprising 16%
• Toxic Shock Syndrome 16%
• Challenges in Healthcare 16%
• Postinfectious Sequelae 16%
• Murine Typhus 16%
• Balanced Evaluation 16%

Medicine and Dentistry

• Serositis 100%
• Clinical Decision Support System 100%
• Murine Typhus 100%
• Typhus 33%
• Decision Making 16%
• Sequela 16%
• Clinical Feature 16%
• Infection 16%
• Scoring System 16%
• Kawasaki Disease 16%
• Toxic Shock Syndrome 16%
• COVID-19 16%
• Disease 16%
• Emergency Department 16%

Biochemistry, Genetics and Molecular Biology

• Artificial Intelligence 100%
• Clinical Decision Support System 100%
• Decision Making 25%