Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning
<|reference_start|>Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning: 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.<|reference_end|>
@article{hu2024enhancing,
    title={Enhancing Uncertain Demand Prediction in Hospitals Using Simple and
  Advanced Machine Learning},
    author={Annie Hu, Samuel Stockman, Xun Wu, Richard Wood, Bangdong Zhi, Oliver
  Y. Ch'en},
    journal={arXiv preprint arXiv:2404.18670},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18670},
    primaryClass={cs.LG stat.AP}
}