Predicting pressure injury using nursing assessment phenotypes and machine learning methods

Keyphrases

• Machine Learning Techniques 100%
• Pressure Injury 100%
• Nursing Assessment 100%
• Pressure Injury Prevention 37%
• Machine Learning Prediction 25%
• Clinical Features 12%
• Adverse Outcomes 12%
• Patient Assessment 12%
• Glasgow Coma Scale 12%
• Hospitalized Patients 12%
• Patient Safety 12%
• Common Complications 12%
• Early Treatment 12%
• Early Prevention 12%
• Clinical Scenarios 12%
• Serious Complications 12%
• Injury Rate 12%
• Prediction Accuracy 12%
• Prediction Model 12%
• Risk Assessment Tool 12%
• Healthcare Organizations 12%
• Model Performance 12%
• Electronic Health Record Data 12%
• Model-driven Development 12%
• Clinical Datasets 12%
• Machine Learning Based 12%
• Non-hospital 12%
• Assessment Data 12%
• Multiple Machines 12%
• Level of Consciousness 12%
• Random Forest Regression 12%
• 5-fold Cross Validation 12%
• Safety Metrics 12%
• Advanced Statistical Methods 12%
• Integrated Health Care 12%
• Learning-based Predictive Model 12%
• Hospital-acquired Pressure Injury 12%
• Quality of nursing Care 12%

Nursing and Health Professions

• Pressure Injury 100%
• Nursing Assessment 100%
• Injury 8%
• Clinical Feature 8%
• Area under the Curve 8%
• Glasgow Coma Scale 8%
• Adverse Outcome 8%
• Health Care Organization 8%
• Patient Assessment 8%
• Patient Safety 8%
• Electronic Health Record 8%
• Injury Prevention 8%
• Statistical Analysis 8%
• Random Forest 8%
• Quality of Nursing Care 8%