通过自然语言处理加强美国的孕产妇健康监测
Rebecca Horgan1, Tetsuya Kawakita1, George Saade1
1Department of Obstetrics and Gynecology, Macon and Joan Brock Virginia Health Sciences, Old Dominion University, Norfolk, Virginia, United States.
American journal of perinatology
|December 5, 2025
概括
改善美国的孕产妇健康监测至关重要. 自然语言处理可以将临床笔记转化为结构化数据,提高公共卫生监测的准确性和及时性.
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:
- 产妇健康结果是医疗保健质量和社会福祉的关键指标.
- 美国目前的孕产妇健康监测存在不准确性,限制了其临床效用,并阻碍了政策反应.
- 尽管电子健康记录可用,但数据的局限性阻碍了及时的干预和创新.
研究的目的:
- 探索自然语言处理 (NLP) 的应用,以加强孕产妇健康监测.
- 展示NLP如何将非结构化的临床叙事文本转化为结构化,可分析的数据.
- 突出人工智能驱动系统在提高孕产妇健康数据的准确性和及时性方面的潜力.
主要方法:
- 使用基于规则的语言处理和机器学习技术的组合.
- 应用NLP从非结构化的临床笔记和电子健康记录中提取关键见解.
- 使用处理数据开发预测模型和实时公共卫生监测系统.
主要成果:
- NLP可以有效地将叙事性临床文本转化为适合分析的结构化数据.
- 提出的方法显示出提高孕产妇健康监测的准确性和及时性的潜力.
- 由人工智能驱动的方法可以克服传统数据收集方法的局限性.
结论:
- 自然语言处理为当前孕产妇健康监测中的不准确性提供了一个有希望的解决方案.
- 实施NLP可以带来更有效的妇产科公共卫生监测系统.
- 伦理考虑,包括隐私,偏见控制和验证,对于在医疗保健中负责任地使用NLP至关重要.
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