  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".
