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与阿片类药物相关的致命过量服用的时空空间预测:针对建模和评估的最佳实践

Kyle Heuton1, Jyontika Kapoor2, Shikhar Shrestha3

  • 1Department of Computer Science, Tufts University, Medford, Massachusetts, United States.

American journal of epidemiology
|September 14, 2024
PubMed
概括

新的指标可以更好地指导阿片类药物过量公共卫生干预措施. 百分比最佳可能覆盖率 (%BPR) 度量比RMSE等传统错误度量更有效地确定优先领域.

关键词:
机器学习是机器学习.这是一种阿片类阿片类药物.过量服用过量服用过量预测建模预测建模公共卫生实践 公共卫生实践空间时间预测预测.

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