哥伦比亚主要的精神活性物质消费模式:基于深度神经网络的集群导向嵌入方法
Kevin Palomino1, Carmen R Berdugo1, Jorge I Vélez1
1Department of Industrial Engineering, Universidad del Norte, Barranquilla, Colombia.
PloS one
|August 18, 2023
概括
这项研究引入了一种新的AI算法,用于绘制哥伦比亚的精神活性物质 (PAS) 使用模式. 调查结果揭示了不同的区域消费集群,为目标公共卫生政策提供了信息.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 神经科学是一个神经科学.
背景情况:
- 与精神活性物质 (PAS) 相关的健康事件的全球增加.
- 在哥伦比亚,非法毒品,酒精和烟草的使用大幅增加.
- 需要先进的方法来识别药物消费模式,以制定政策.
研究的目的:
- 介绍一种基于深度神经网络的集群导向嵌入算法.
- 识别精神活性物质消费和滥用的一般和空间模式.
- 提取相关特征并确定消费集群.
主要方法:
- 使用了一种新的深度神经网络算法与自动编码器和空间技术.
- 将算法应用于49600个哥伦比亚家庭的大数据集.
- 估计和地理上代表的PAS消费和滥用流行率.
主要成果:
- 确定了影响PAS消费的关键因素:地区,性别,住房,社会经济地位,年龄和财务状况.
- 发现了三种不同的PAS消费和/或滥用模式.
- 揭示了与特定的地理和社会环境背景相关的毒品使用的空间度.
结论:
- 开发的算法有效地识别了PAS消费的一般和空间模式.
- 调查结果为基于证据的国家政策提供了关键的见解.
- 根据文化,地理和社会因素,可以制定有针对性的干预措施.
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