Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors

Key Points

• The aim is to enhance the classification of survivor-reported pain and fatigue using optimized prompting strategies in large language models.
• Utilizing prompt engineering to refine language model inputs.
• Analyzing pain and fatigue-related functional impact reports from survivors.
• Implementing automated analysis techniques for symptom narratives.
• Improved accuracy in classifying pain and fatigue symptoms.
• Enhanced context-aware interpretation of survivor narratives.
• Potential for scalable support in monitoring symptoms and care.