基于机器学习的急救医疗救护车调度分类模型的构建

Health data science Pub Date : 2023-03-15 eCollection Date: 2023-01-01 DOI:10.34133/hds.0008
Han Wang, Qin Xiang Ng, Shalini Arulanandam, Colin Tan, Marcus E H Ong, Mengling Feng
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引用次数: 0

摘要

背景:紧急医疗服务(EMS)呼叫中心的专家负责调度救护车,他们往往难以在有限的时间内收集到的信息来判断病例的严重程度。虽然有一些协议可以指导他们做出决策,但观察到的表现仍可能缺乏灵敏度和特异性。众所周知,机器学习模型可以捕捉微妙的复杂关系,训练有素的数据模型可以在一瞬间做出准确的预测:在本研究中,我们提出了一种概念验证方法来构建机器学习模型,以更好地预测急诊病例的严重程度。我们使用了新加坡国家紧急呼叫中心在 2018 年至 2020 年期间接收的超过 36 万条结构化紧急呼叫中心病例记录。我们利用呼叫记录创建了特征,并训练了多个机器学习模型:随机森林模型取得了最佳性能,与呼叫中心专家相比,过度分流率绝对值降低了 15%,同时保持了类似水平的分流不足率:该模型可作为调度员的决策支持工具,与当前协议一起优化救护车调度分流和急救资源的利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building a Machine Learning-based Ambulance Dispatch Triage Model for Emergency Medical Services.

Background: In charge of dispatching the ambulances, Emergency Medical Services (EMS) call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time. Although there are protocols to guide their decision-making, observed performance can still lack sensitivity and specificity. Machine learning models have been known to capture complex relationships that are subtle, and well-trained data models can yield accurate predictions in a split of a second.

Methods: In this study, we proposed a proof-of-concept approach to construct a machine learning model to better predict the acuity of emergency cases. We used more than 360,000 structured emergency call center records of cases received by the national emergency call center in Singapore from 2018 to 2020. Features were created using call records, and multiple machine learning models were trained.

Results: A Random Forest model achieved the best performance, reducing the over-triage rate by an absolute margin of 15% compared to the call center specialists while maintaining a similar level of under-triage rate.

Conclusions: The model has the potential to be deployed as a decision support tool for dispatchers alongside current protocols to optimize ambulance dispatch triage and the utilization of emergency ambulance resources.

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CiteScore
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