发展和验证类风湿性关节炎病例定义:使用初级保健电子病历数据的机器学习方法
Anh N Q Pham1,2,3, Claire E H Barber4, Neil Drummond4,5
1Department of Health Sciences, Simon Fraser University, Burnaby, Canada. nqp@sfu.ca.
BMC medical informatics and decision making
|November 28, 2024
概括
使用初级保健电子病历开发了类风湿性关节炎 (RA) 的新病例定义. 这种经过验证的工具准确地识别RA病例,以改善监测和研究.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 医疗保健中的机器学习
背景情况:
- 类风湿性关节炎 (RA) 是一种慢性炎症性疾病,通常是由初级保健提供者首次发现的.
- 在初级保健中精确识别RA对于及时诊断和管理至关重要.
- 现有的监测方法可能无法在初级保健机构内完全捕捉RA病例.
研究的目的:
- 使用国家初级保健监测数据,开发和验证类风湿性关节炎 (RA) 的病例定义.
- 利用机器学习技术在电子病历中识别RA患者.
- 为流行病学研究和RA监测创造一个可靠的工具.
主要方法:
- 利用来自加拿大初级保健哨兵监测网络 (CPCSSN) 的结构化电子病历 (EMR) 数据.
- 采用了三步的机器学习过程:"字袋"特征生成,递归特征消除和基于树的分类 (XGBoost).
- 使用嵌套交叉验证和专家生成的参考集验证了案例定义.
主要成果:
- XGBoost算法生成了最佳的RA病例识别.
- 获得了人类可读的病例定义:患者病历数据中≥2例"风湿性"病例.
- 实现了高有效度指标:81.6%的灵敏度,98.0%的特异性,76.3%的PPV和98.6%的NPV.
结论:
- 从初级保健EMR数据使用XGBoost算法成功开发了类风湿性关节炎 (RA) 的高度有效的病例定义.
- 这一定义是CPCSSN数据集中的未来流行病学研究和RA管理监测的可靠工具.
- 这些发现支持使用机器学习来开发初级保健机构中强大的病例定义.
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