Machine Learning in Discharge Planning for Stroke Patients: A Review of Feature Representation and Aggregation Strategies With Suggestions for Improvement

Key Points

• The aim is to review and evaluate feature representation and aggregation strategies relevant to machine learning in discharge planning for stroke patients.
• Conducted a review of existing literature on machine learning applications in discharge planning for stroke patients.
• Analyzed different feature representation techniques and aggregation strategies used in the field.
• Identified gaps and suggested improvements for future machine learning implementations.
• Highlighted various feature representation methods currently employed in discharge planning.
• Discussed the effectiveness of different aggregation strategies in enhancing machine learning outcomes.
• Recommended specific improvements to existing methodologies for better performance.