Predicting User-specific Future Activities using LSTM-based Multi-label Classification
This work addresses activity prediction for nurses and patients in healthcare, but it appears incremental as it builds on existing methods with specific adaptations.
The paper tackles predicting user-specific future activities in healthcare using an LSTM-based multi-label classifier with a 2-stage training approach, achieving validation metrics such as 31.58% accuracy and 60.38% F1 score.
User-specific future activity prediction in the healthcare domain based on previous activities can drastically improve the services provided by the nurses. It is challenging because, unlike other domains, activities in healthcare involve both nurses and patients, and they also vary from hour to hour. In this paper, we employ various data processing techniques to organize and modify the data structure and an LSTM-based multi-label classifier for a novel 2-stage training approach (user-agnostic pre-training and user-specific fine-tuning). Our experiment achieves a validation accuracy of 31.58\%, precision 57.94%, recall 68.31%, and F1 score 60.38%. We concluded that proper data pre-processing and a 2-stage training process resulted in better performance. This experiment is a part of the "Fourth Nurse Care Activity Recognition Challenge" by our team "Not A Fan of Local Minima".