论文标题
使用机器学习和模拟对急诊科进行对患者流量进行建模
Modeling patient flow in the emergency department using machine learning and simulation
论文作者
论文摘要
最近，机器学习（ML）和模拟的组合引起了很多关注。本文介绍了ML在模拟中的新应用，以改善急诊科内的患者流量（ED）。在实际ED模拟模型中使用的ML模型来量化ED避开患者在住院长度（LOS）和门到折叠时间（DTDT）的影响，以应对从ED预测患者入院的一种预测。使用一组六个功能训练的ML模型，包括患者年龄，到达日，一天中的到达小时和分类水平。预测模型使用了决策树（DT）模型，该模型经过历史数据的训练可以达到75％的精度。从DT提取的规则集在仿真模型中编码。考虑到一定的免费住院床的可能性，然后将预测的被录取的患者从ED到住院单位拉出，以减轻ED内的拥挤。使用的策略和添加特定的ED资源的结合分别降低了LOS和DTDT的9.39％和8.18％。
Recently, the combination of machine learning (ML) and simulation is gaining a lot of attention. This paper presents a novel application of ML within the simulation to improve patient flow within an emergency department (ED). An ML model used within a real ED simulation model to quantify the effect of detouring a patient out of the ED on the length of stay (LOS) and door-to-doctor time (DTDT) as a response to the prediction of patient admission to the hospital from the ED. The ML model trained using a set of six features including the patient age, arrival day, arrival hour of the day, and the triage level. The prediction model used a decision tree (DT) model, which is trained using historical data achieves a 75% accuracy. The set of rules extracted from the DT are coded within the simulation model. Given a certain probability of free inpatient beds, the predicted admitted patient is then pulled out from the ED to inpatient units to alleviate the crowding within the ED. The used policy combined with adding specific ED resources achieve 9.39% and 8.18% reduction in LOS and DTDT, respectively.
