用简单模型预测肯塔基州过量用药，助力应急资源调配
Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdoses in Emergency Medical Services Data
- 基于县和区级数据，用简单模型预测未来过量用药次数
- 在不同地区均实现低误差预测，效果稳定可靠
- 仅需常见公共健康数据，适合政府快速部署
本文针对肯塔基州应急医疗服务体系（EMS）记录的疑似阿片类药物过量事件，开展机器学习与时间序列预测研究，旨在准确预测未来疑似过量用药病例数。通过县级和区级聚合数据，对不同时间间隔进行未来病例数预测。评估了多种复杂度的模型以最小化预测误差，并测试了多种与阿片类药物及公共卫生相关的附加协变量对模型性能的影响。结果表明，即使在数据有限的情况下，也能对不同类型地区生成误差较低的预测，且使用常见可得协变量和相对简单的预测模型即可实现高精度表现。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。