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.

翻译：早期且及时的医疗服务需求预测不仅影响资源有效配置，还影响临床决策及患者体验。然而，准确预测医疗服务需求是全球医院普遍面临的挑战，部分原因是需求的时变波动性，部分原因在于难以提前捕捉变化趋势。针对这一问题，本文开发了两种方法：一种相对简单的时变线性模型，以及一种更先进的神经网络模型。前者基于星期几、过去7天到达模式等因素，预测未来一周内每小时的病人到达数；后者则采用长短期记忆（LSTM）模型，捕捉历史数据与三天预测窗口之间的非线性关系。我们将所提出的两种方法的预测能力与两种简单方法——降秩向量自回归（VAR）模型和TBATS模型——进行了比较。利用以色列拉姆巴姆医疗中心的医疗服务需求数据，我们的结果表明两种模型均能有效捕捉患者需求的每小时变化。此外，线性模型因其简洁的架构更具可解释性，而LSTM模型通过准确建模每周季节性趋势，获得了更低的预测误差。综合来看，本研究揭示了机器学习在预测时变医疗服务需求中的实用性；同时，利用机器学习可提前三天或一周以较高准确度（约4名患者）预测医疗服务需求。