Utilisation of machine learning to predict surgical candidates for the treatment of childhood upper airway obstruction

Keyphrases

• Machine Learning 100%
• Surgical Candidate 100%
• Adenotonsillectomy 100%
• Upper Airway Obstruction 100%
• Polysomnography 66%
• Surgeons 33%
• Supportive Care 33%
• Behavior Regulation 33%
• After Surgery 33%
• Cluster-based 33%
• Data-driven Analysis 33%
• Watchful Waiting 33%
• Emotional Regulation 33%
• Thoracoabdominal Asynchrony 33%
• Pattern Dynamics 33%
• Apnea-hypopnea Index 33%
• Unnecessary Surgery 33%
• End-tidal Carbon Dioxide 33%
• Heart Period 33%
• Symptom-based 33%
• Sleep-related Hypoventilation 33%
• Overnight Polysomnography 33%
• Data-driven Approach 33%

Medicine and Dentistry

• Symptom 100%
• Upper Respiratory Tract Obstruction 100%
• Polysomnography 42%
• Adenotonsillectomy 42%
• Disease 14%
• Surgeon 14%
• Supportive Care 14%
• Hypopnea 14%
• Watchful Waiting 14%
• Heart Cycle 14%
• Hypoventilation 14%
• Asynchrony 14%
• Carbon Dioxide 14%
• Apnea 14%