Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department

Keyphrases

• Crohn's Disease 100%
• Emergency Department 100%
• Machine Learning 100%
• Patient Selection 100%
• Abdominopelvic Computed Tomography 100%
• Actionable Findings 70%
• Clinical Variables 20%
• Retrospective Cohort Study 10%
• Mean Arterial Pressure 10%
• Beats per Minute 10%
• Clinical High Risk 10%
• Artificial Intelligence 10%
• Immediate Intervention 10%
• Perforation 10%
• Fistula 10%
• Machine Learning Approach 10%
• Neutrophil-to-lymphocyte Ratio 10%
• Bowel Obstruction 10%
• Risk Stratification System 10%
• Abdominal Abscess 10%
• Ionizing Radiation Effects 10%
• Area under the Receiver Operating Characteristic (AUROC) 10%

Medicine and Dentistry

• Emergency Department 100%
• Computer Assisted Tomography 100%
• Patient with Crohn's Disease 100%
• Retrospective Cohort Study 10%
• Mean Arterial Pressure 10%
• Risk Stratification 10%
• Fistula 10%
• Ionizing Radiation 10%
• Identified Patient 10%
• Radiation Exposure 10%
• Bowel Obstruction 10%
• Abdominal Abscess 10%
• Lymphocyte 10%
• Neutrophil 10%