An integrated deep learning and stochastic optimization approach for resource management in team-based healthcare systems
作者：
Highlights：
• Predicts the imposed workload of patients to providers using supervised and unsupervised methods.
• Generates robust features by using deep learning stacked auto-encoders.
• Proposes a deep multi-task learning approach to improve prediction performance.
• Uses a two-stage stochastic resource allocation and workload optimization model.
• Generates scenarios based on uncertain demand and simulation.
摘要
•Predicts the imposed workload of patients to providers using supervised and unsupervised methods.•Generates robust features by using deep learning stacked auto-encoders.•Proposes a deep multi-task learning approach to improve prediction performance.•Uses a two-stage stochastic resource allocation and workload optimization model.•Generates scenarios based on uncertain demand and simulation.
论文关键词：Deep learning,Stacked autoencoders,Multi-task learning,Demand prediction,Stochastic optimization,Resource planning
论文评审过程：Received 18 January 2021, Revised 17 May 2021, Accepted 15 September 2021, Available online 21 September 2021, Version of Record 3 October 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115924
