Enhancing Decision Making with Deep Reinforcement Learning in a Context of Novel Coronavirus Outbreak: an Example in Emergency Department

H. Jiang, William Yu Chung Wang, T. Goh, Jie Zhu
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Abstract

Physicians in hospitals are expected to improve treatment outcome and reduce health care costs. Information systems have been widely adopted in hospitals but not been properly integrated to provide information for decision support. The objective of this research is trying to validate the feasibility of enhancing hospital resource planning system in decision support by utilizing data stored in multiple systems in the hospital with a deep reinforcement learning approach to assist medical practitioner making a more accurate and efficient decision. Following the Design Science Research Method, this research is going to build an artefact to utilize data from electronic health record (EHR) and hospital resource planning (HRP) to provide medical decision support in the emergency department setting.
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新型冠状病毒疫情背景下深度强化学习增强决策:以急诊科为例
人们期望医院的医生改善治疗效果,降低医疗费用。信息系统已在医院广泛采用,但尚未得到适当整合,无法为决策支持提供信息。本研究的目的是通过深度强化学习的方法,利用存储在医院多个系统中的数据,来验证增强医院资源规划系统在决策支持中的可行性,以帮助医生做出更准确、更有效的决策。本研究以设计科学研究方法为基础,利用电子健康档案(EHR)和医院资源规划(HRP)的资料,建立一个人工系统,为急诊科提供医疗决策支持。
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