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儿童致命伤害叙述的结构化主题建模,使用机器学习与县级人口指标来加强预防策略.

Gia E Barboza-Salerno1, Karla J Shockley McCarthy2, Taylor R Harrington3

  • 1College of Social Work, The Ohio State University, Columbus, OH, USA.

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概括

使用机器学习分析儿童死亡报告,发现了11个关键主题. 社会人口统计学因素影响了伤害类型,突出了针对儿童安全的有针对性的预防策略的需求.

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虐待儿童 虐待儿童死亡事故是死亡事故.杀人部门的杀人案件.政策 政策 政策 政策预防 预防 预防滥用物质滥用物质滥用物质滥用物质滥用物质滥用物质滥用物质

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科学领域:

  • 公共卫生 公共卫生
  • 计算语言学 计算语言学
  • 社会学 社会学 社会学

背景情况:

  • 儿童致命伤害缺乏干预的系统分类方法.
  • 死亡报告中的非结构化的文字叙述妨碍了分析.
  • 现有的数据缺乏儿童死亡的社会人口统计背景.

研究的目的:

  • 开发和应用机器学习模型来对儿童死亡情况进行分类.
  • 整合社会人口统计数据与儿童死亡报告进行增强分析.
  • 确定与儿童死亡相关的趋势和风险因素.

主要方法:

  • 来自宾夕法尼亚州的453份儿童死亡报告 (2016-2023) 的分析.
  • 整合美国社区调查 (ACS) 数据用于县级社会人口统计数据.
  • 自然语言处理 (NLP) 和结构化主题建模 (STM) 的应用.

主要成果:

  • 在儿童死亡报告中确定了11个不同的主题,包括物质滥用 (13.4%) 和与睡眠有关的死亡 (12%).
  • 观察到的时间变化:家长疏忽导致的谋杀案减少,而物质滥用增加.
  • 贫困和非白人人口与较高的枪支相关伤害和严重创伤伤害率相关.

结论:

  • 使用社会人口统计数据进行计算文本分析,为儿童死亡提供了可操作的见解.
  • 机器学习模型可以加强死亡监控系统.
  • 调查结果可以为有针对性的预防策略提供信息,以提高儿童安全.