用于预测紧急医疗服务数据中疑似阿片类药物过量事件的机器学习模型的实现与评估
Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdoses in Emergency Medical Services Data
AI总结：
本研究利用机器学习和时间序列预测方法，基于肯塔基州EMS数据预测疑似阿片类药物过量事件数量，评估多种模型和协变量，发现简单模型结合常见协变量即可实现高精度预测。
摘要：
我们展示了在机器学习和时间序列预测领域的工作，旨在准确预测肯塔基州紧急医疗服务（EMS）记录的疑似阿片类药物过量事件的未来数量。预测有助于政府机构妥善准备和分配与阿片类药物过量相关的资源。我们的方法使用县和地区级别的疑似阿片类药物过量接触事件聚合数据，并预测不同时间间隔的未来数量。我们评估了不同复杂度的模型以最小化预测误差。我们测试了多种与阿片类药物过量及公共卫生相关的额外协变量，以确定它们对模型性能的影响。我们的评估表明，对于不同类型的地区，可以以有限的误差生成有用的预测，并且使用常见的协变量和相对简单的预测模型即可实现高性能。
英文摘要：
We present efforts in the fields of machine learning and time series forecasting to accurately predict counts of future suspected opioid overdoses recorded by Emergency Medical Services (EMS) in the state of Kentucky. Forecasts help government agencies properly prepare and distribute resources related to opioid overdoses. Our approach uses county and district level aggregations of suspected opioid overdose encounters and forecasts future counts for different time intervals. Models with different levels of complexity were evaluated to minimize forecasting error. A variety of additional covariates relevant to opioid overdoses and public health were tested to determine their impact on model performance. Our evaluation shows that useful predictions can be generated with limited error for different types of regions, and high performance can be achieved using commonly available covariates and relatively simple forecasting models.
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