机器学习工具在证据中使用 综合性和性别多元人口中烟草使用:算法开发和验证
Shaoying Ma1, Shuning Jiang2, Olivia Yang2
1Center for Tobacco Research, The Ohio State University Comprehensive Cancer Center, Columbus, OH, United States.
JMIR formative research
|January 24, 2024
概括
自然语言处理 (NLP) 工具可以加速针对LGBTQ+群体的烟草政策研究的综合. 这项试点研究证明了NLP.
科学领域:
- 公共卫生 公共卫生
- 生物医学信息学 生物医学信息学
- 社会科学 社会科学 社会科学
背景情况:
- 越来越多的烟草研究对证据综合提出了挑战,特别是对于像LGBTQ+个人这样的特定人群.
- 对性和性别多样性社区来说,有效地总结和传播发现至关重要.
研究的目的:
- 开发用于烟草政策研究的自然语言处理 (NLP) 算法.
- 为烟草研究创建一个专门的语义数据库.
- 识别研究缺口,加快LGBTQ+群体的证据综合.
主要方法:
- 使用2993个烟草研究摘要构建了一个特定领域的语义数据库.
- 一个NLP模型被训练来提取命名实体和上下文关系.
- 研究结果与正在进行的关于LGBTQ+烟草政策的系统审查进行了比较.
主要成果:
- 确定了33项与性和性别多样化个体尼古丁和烟草使用相关的研究.
- 发现,在这个人口群体中,使用因果推理进行政策影响评估的研究很少.
- 研究主要以美国数据为特征,重点关注香烟,并且在各个年龄组中均分布.
结论:
- 在加快证据综合和识别烟草政策研究缺口方面,NLP工具显示出巨大的潜力.
- 这项试点研究证明了NLP在分析复杂研究环境中的有效性.
- 需要进一步的研究来统计验证NLP工具的性能,以便更广泛地应用.
关键词:
这是一个LGBTQ++社区.这是一个双性恋的双性恋.证据综合 证据综合同志同志的同志同志的同志同志的同志同志的同志同志的同志同志的同志一个女同性恋的女同性恋.机器学习是机器学习.自然语言处理自然语言处理.奇怪的 奇怪的 奇怪的性和性别多样化的人口.控制烟草烟草的控制变性人是指变性人,是指变性人.更多相关视频
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