让具有专业或实践经验的利益相关者参与改善枪支暴力词典开发.
Nicole Cook1, Frances M Biel1, Kerry Ann Bet1
1OCHIN, PO Box 5426, Portland, OR, 97228, United States, 1 9546126511.
JMIR formative research
|April 21, 2025
概括
要了解枪支暴力对公共卫生的影响,需要包括那些有间接接触的人. 使用自然语言处理 (NLP) 分析的电子健康记录笔记可以捕获这些暴露数据. 参与多种利益相关者是有效的NLP词汇开发枪支暴力研究的关键.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 关于枪支暴力的公共卫生研究往往忽视了二次暴露的个体,限制了全面的负担评估.
- 电子健康记录 (EHR) 包含有价值的临床笔记,可以捕获有关枪支暴力暴露的数据.
- 自然语言处理 (NLP) 提供了一种方法,可以从非结构化的EHR临床笔记中提取有意义的信息.
研究的目的:
- 探索EHR临床笔记和NLP的潜力,以识别二次接触枪支暴力的个人.
- 通过吸引各种利益相关者来开发和完善枪支暴力的NLP词典.
主要方法:
- 吸引了包括社区成员和医疗保健专业人员在内的各种利益相关者,以确定NLP词典开发的相关关键词.
- 利用自然语言处理技术来分析EHR临床笔记,寻找枪支暴力暴露的指标.
- 基于利益相关者的投入,代开发和验证NLP词典.
主要成果:
- 多种利益相关者的参与显著提高了NLP词典开发关键词的质量和相关性.
- 调查结果表明,EHR临床笔记是识别二次枪支暴力暴露的可行数据来源.
- 利益相关者的意见对于提高NLP模型在识别枪支暴力环境中的准确性至关重要.
结论:
- 利用EHR临床笔记与NLP,根据各种利益相关者的意见,可以改善对枪支暴力的公共卫生负担的测量.
- 未来的研究应该专注于验证NLP衍生数据对二次性枪支暴力暴露及其公共卫生影响.
- 在NLP开发中的协作方法对于从临床数据中捕捉细微的公共卫生问题至关重要.
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