使用自然语言处理方法来预测2019年俄俄州梅毒疾病干预专家记录中包含的主题
Payal Chakraborty, Xia Ning, Mary McNeill1
1Ohio Department of Health, Columbus, OH.
Sexually transmitted diseases
|February 11, 2025
概括
自然语言处理 (NLP) 模型可以有效地分析疾病干预专家 (DIS) 笔记,用于控制性传播感染. 机器学习准确地识别了梅毒记录中的关键主题,改善了数据的可访问性.
科学领域:
- 公共卫生 公共卫生
- 计算语言学 计算语言学
- 流行病学 流行病学
背景情况:
- 疾病干预专家 (DIS) 记录中的自由文本注释包含用于性传播感染 (STI) 控制的宝贵数据.
- 目前对这些笔记的手动分析效率低下,容易出现错误.
研究的目的:
- 开发和评估自然语言处理 (NLP) 和机器学习 (ML) 模型,用于分析DIS自由文本笔记.
- 在这些记录中确定与STI传输和案件调查相关的关键主题.
主要方法:
- 利用NLP技术分析了1987年俄俄州DIS梅毒记录,从2019年开始.
- 使用的术语频率-反向文档频率 (TF-IDF) 和GloVe嵌入式用于文本表示.
- 训练并比较天真的贝叶斯,支持向量机 (SVM) 和长短期记忆 (LSTM) 模型用于主题识别.
主要成果:
- 该LSTM模型在识别大多数主题方面表现出卓越的表现,在"物质使用"方面达到高准确度 (97%).
- 在传统的统计模型中,SVM模型表现最好.
- 模型的表现仅限于识别不太常见的主题,例如"酒精使用"或"护理延迟".
结论:
- 机器学习模型在从DIS笔记中提取有价值的信息以控制STI方面表现有希望.
- 这项研究代表了利用NLP对公共卫生记录进行增强分析的基本步骤.
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