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首页> 外文期刊>JMIR Medical Informatics >Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study
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Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

机译：利用机器学习方法对慢性呼吸道疾病患者的峰门诊和急诊课程访问预测：回顾性队列研究

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Background The overcrowding of hospital outpatient and emergency departments (OEDs) due to chronic respiratory diseases in certain weather or under certain environmental pollution conditions results in the degradation in quality of medical care, and even limits its availability. Objective To help OED managers to schedule medical resource allocation during times of excessive health care demands after short-term fluctuations in air pollution and weather, we employed machine learning (ML) methods to predict the peak OED arrivals of patients with chronic respiratory diseases. Methods In this paper, we first identified 13,218 visits from patients with chronic respiratory diseases to OEDs in hospitals from January 1, 2016, to December 31, 2017. Then, we divided the data into three datasets: weather-based visits, air quality-based visits, and weather air quality-based visits. Finally, we developed ML methods to predict the peak event (peak demand days) of patients with chronic respiratory diseases (eg, asthma, respiratory infection, and chronic obstructive pulmonary disease) visiting OEDs on the three weather data and environmental pollution datasets in Guangzhou, China. Results The adaptive boosting-based neural networks, tree bag, and random forest achieved the biggest receiver operating characteristic area under the curve, 0.698, 0.714, and 0.809, on the air quality dataset, the weather dataset, and weather air quality dataset, respectively. Overall, random forests reached the best classification prediction performance. Conclusions The proposed ML methods may act as a useful tool to adapt medical services in advance by predicting the peak of OED arrivals. Further, the developed ML methods are generic enough to cope with similar medical scenarios, provided that the data is available.
机译：背景技术由于某些天气或某些环境污染条件下的慢性呼吸系统疾病，医院门诊和急诊部门（OEDs）的过度拥挤导致医疗质量下降，甚至限制其可用性。目的帮助经理管理人员在空气污染和天气的短期波动后，在短期波动后的过度医疗需求期间安排医疗资源分配，我们使用机器学习（ML）方法来预测慢性呼吸道疾病患者的峰值抵达。方法本文在2016年1月1日至2017年12月31日，我们首先将慢性呼吸道疾病患者从医院患者鉴定了13,218名。然后，我们将数据分为三个数据集：基于天气的访问，空气质量 - 基于访问和天气空气质量的访问。最后，我们开发了预测慢性呼吸系统疾病（例如，哮喘，呼吸道感染和慢性阻塞性肺病的患者的峰事件（峰值需求日）的峰值事件（例如，哮喘，呼吸道感染和慢性阻塞性肺部疾病）在广州的三个天气数据和环境污染数据集上进行了慢性呼吸系统疾病（例如哮喘，呼吸道感染和慢性阻塞性肺病），中国。结果基于自适应升压的神经网络，树袋和随机森林在空气质量数据集，天气数据集和天气空气质量数据集中实现了曲线下的最大接收器操作特性区域，0.698,0.714和0.809 。总体而言，随机森林达到了最佳分类预测性能。结论提出的ML方法可以作为一种通过预测OED港的峰值提前适应医疗服务的有用工具。此外，发达的ML方法足够通用以应对类似的医疗场景，条件是数据可用。

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