分析电子医疗记录以提取孕前疾病和妊娠并发症:朝着学习健康系统的方向
Yitayeh Belsti1, Lisa Moran1, Aya Mousa1
1Monash Centre for Health Research and Implementation (MCHRI), Faculty of Medicine, Nursing and Health Sciences Monash University Melbourne Victoria Australia.
Learning health systems
|April 18, 2025
概括
超过77%的孕妇患有疾病,维生素缺乏和超重是最常见的. 怀孕期间的多种疾病与不良结果有关,突出显示了改善孕产妇医疗保健系统的需要.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 产科和妇科 产科和妇科
背景情况:
- 预先存在的和与怀孕相关的医疗条件往往并存,导致孕妇多病症.
- 电子医疗记录 (EMR) 包含有关母亲健康的宝贵数据,但通常是半结构化的.
- 从EMR中自动提取信息对于了解妊娠并发症至关重要.
研究的目的:
- 推进学习健康系统,从EMR中自动提取信息.
- 为了确定怀孕期间常见疾病的患病率.
- 调查疾病和与怀孕有关的并发症之间的关联.
主要方法:
- 利用自然语言处理 (NLP) 来从半结构化EMR中提取病率数据.
- 包括来自莫纳什健康产科医院的48502名孕妇 (2016-2021年).
- 采用千平方测试和k-means集群来分析关联并确定与妊娠糖尿病 (GDM) 相关的并发症集群.
主要成果:
- 维生素缺乏 (28.9%),超重 (28.7%) 和肥胖 (22.7%) 是最常见的并发症.
- 77.5%的孕妇患有一种或多种疾病;多种疾病与超重,肥胖,维生素缺乏,甲状腺疾病,PCOS,GDM等有关.
- 先进的母亲年龄 (≥35),超重,肥胖,维生素缺乏,甲状腺疾病,喘,子宫疾病,血液疾病,精神疾病和PCOS与GDM有关.
结论:
- 超过四分之三的孕妇患有疾病,这可能会影响怀孕的结果.
- 该项目增强学习卫生系统基础设施,以提供高价值,成本效益的孕产妇护理.
- 从EMR中自动提取发病率数据是改善孕产妇健康结果的关键.
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