  Early and timely prediction of patient care demand not only affects effective
resource allocation but also influences clinical decision-making as well as
patient experience. Accurately predicting patient care demand, however, is a
ubiquitous challenge for hospitals across the world due, in part, to the
demand's time-varying temporal variability, and, in part, to the difficulty in
modelling trends in advance. To address this issue, here, we develop two
methods, a relatively simple time-vary linear model, and a more advanced neural
network model. The former forecasts patient arrivals hourly over a week based
on factors such as day of the week and previous 7-day arrival patterns. The
latter leverages a long short-term memory (LSTM) model, capturing non-linear
relationships between past data and a three-day forecasting window. We evaluate
the predictive capabilities of the two proposed approaches compared to two
na\"ive approaches - a reduced-rank vector autoregressive (VAR) model and the
TBATS model. Using patient care demand data from Rambam Medical Center in
Israel, our results show that both proposed models effectively capture hourly
variations of patient demand. Additionally, the linear model is more
explainable thanks to its simple architecture, whereas, by accurately modelling
weekly seasonal trends, the LSTM model delivers lower prediction errors. Taken
together, our explorations suggest the utility of machine learning in
predicting time-varying patient care demand; additionally, it is possible to
predict patient care demand with good accuracy (around 4 patients) three days
or a week in advance using machine learning.
