Optimizing Endotracheal Suctioning Classification: Leveraging Prompt Engineering in Machine Learning for Feature Selection
Conference paper
Islam, M. R., Ferodous, A. M., Hossain, S., Alnajjar, F. and Ahad, M. 2024. Optimizing Endotracheal Suctioning Classification: Leveraging Prompt Engineering in Machine Learning for Feature Selection. ABC 2024: 6th International Conference on Activity and Behavior Computing. Kyushu, Japan 28 - 31 May 2024 IEEE.
Authors | Islam, M. R., Ferodous, A. M., Hossain, S., Alnajjar, F. and Ahad, M. |
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Type | Conference paper |
Abstract | In a world with an overgrowing elderly population, there exists a critical need for a greater number 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. |
Keywords | Human Activity Recognition; Large Language Model; Generative AI; Machine learning; Nurse-care |
Year | 2024 |
Conference | ABC 2024: 6th International Conference on Activity and Behavior Computing |
Publisher | IEEE |
Accepted author manuscript | License File Access Level Repository staff only |
Publication process dates | |
Completed | 31 May 2024 |
Deposited | 27 Aug 2024 |
Journal citation | In Press |
Copyright holder | © 2024, IEEE |
https://repository.uel.ac.uk/item/8y095
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