Optimizing Endotracheal Suctioning Classification: Leveraging Prompt Engineering in Machine Learning for Feature Selection

In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with require in-person assistance, to accurately identify nursing activities and assess the nursing trainees to help them become proficient. This paper addresses classifying activities in one such procedure, endotracheal suctioning, using skeletal keypoint data of the subject performing the procedure. A multi-step structured prompt engineering method was established and utilized on several LLMs to select or calculate key features from the data. Then the features are passed onto several tuned machine learning models to obtain results. A tuned XGBoost prevailed across all models, achieving 90% accuracy on the validation set.