Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

Keyphrases

• Acute Hospital 33%
• AdaBoost 66%
• After Surgery 33%
• Before Surgery 33%
• Cardiac Patients 33%
• Cardiac Surgery 100%
• Clinical Resources 33%
• Comorbid Conditions 33%
• Complications after Cardiac Surgery 33%
• Ensemble Methods 100%
• EuroSCORE 33%
• EuroSCORE II 33%
• Factor Combination 33%
• Gradient Boosting Model 66%
• Health Care Costs 33%
• Healthcare Resources 33%
• High-risk Surgery 33%
• Higher Level of Care 33%
• Impact on Patients 33%
• Length of Hospital Stay 33%
• Machine Learning Techniques 33%
• Model Sensitivity 33%
• Mortality Rate 33%
• Negative Predictive Value 100%
• Patient Well-being 33%
• Postoperative Complications 100%
• Random Forest 66%
• Random Forest Method 100%
• Scotland 33%
• Severe Postoperative Complications 66%
• Surgical Mortality 33%
• Timing of Surgery 33%

Medicine and Dentistry

• Emergency Care 20%
• EuroSCORE 40%
• Face Surgery 20%
• Health Care Cost 20%
• Heart Surgery 100%
• Mortality Rate 20%
• Postoperative Complication 100%
• Quality of Life 20%
• Surgical Mortality 20%
• Surgical Risk 20%

Pharmacology, Toxicology and Pharmaceutical Science

• Mortality Rate 20%
• Postoperative Complication 100%
• Surgical Mortality 20%