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Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning
April 30, 2024, 4:42 a.m. | Annie Hu, Samuel Stockman, Xun Wu, Richard Wood, Bangdong Zhi, Oliver Y. Ch\'en
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract advanced arxiv challenge clinical cs.lg decision demand experience hospitals however machine machine learning making part patient patient care prediction simple stat.ap type uncertain world
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