利用自动化方法从抽象的出生住院数据中对出生缺陷进行分类
Suzanne M Newton1, Samantha Distler1, Kate R Woodworth1
1Division of Birth Defects and Infant Disorders, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Birth defects research
|November 7, 2023
概括
自动化方法在SET-NET中成功分类了99.6%的出生缺陷,用于监测对孕妇和婴儿的新兴威胁. 这大大减少了对产前暴露的公共卫生监测的手册审查需求.
科学领域:
- 公共卫生监督 公共卫生监督
- 计算流行病学计算流行病学
- 生殖健康 生殖健康
背景情况:
- 孕妇和婴儿新兴威胁监测网络 (SET-NET) 监测产前暴露的结果.
- 自动对出生缺陷进行分类对于及时监测COVID-19,C型肝炎和梅毒至关重要.
- 手动审查医疗数据是耗时和资源密集的.
研究的目的:
- 开发和评估使用NLP和ML对出生缺陷进行分类的自动化过程.
- 为了减少SET-NET数据的手动临床医生审查的工作量.
- 提高识别出生缺陷数据中的模式的效率.
主要方法:
- 在ICD-10-CM代码和自由文本上使用了关键字搜索和模糊匹配 (≥90%分数).
- 应用自然语言处理 (NLP) 和机器学习 (ML) 预测模型用于分类出生缺陷数据.
- 测试了三种不同的数据分类预测模型.
主要成果:
- 在8326个观察结果中,81%与ICD-10-CM代码相匹配,19%需要进一步处理.
- 关键字搜索和模糊匹配对1568个最初不匹配的观测中的1387个进行了分类.
- 一个预测模型正确地分类了80% (144/181) 的剩余不匹配的病例.
结论:
- 自动化方法实现了99.6%的报告出生缺陷的分类,使得更快的模式检测.
- 开发的方法大大减少了手动审查的需要,自动处理1568个观察结果.
- 这些技术为选公共卫生数据提供了快速而准确的解决方案,以告知响应.
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