利用自然语言处理和机器学习从初级保健电子病历中识别慢性疾病
Na Zhang1, Marjan Abbasi2, Sheny Khera1
1Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada.
Scientific reports
|February 11, 2026
概括
将非结构化的临床笔记与机器学习相结合,可以改善初级保健电子病历 (EMR) 中的慢性疾病检测. 这种方法提高了诸如关节炎和呼吸道疾病等疾病的识别.
科学领域:
- 计算健康信息学 计算健康信息学
- 医学数据科学 医学数据科学
- 初级护理研究初级护理研究
背景情况:
- 电子病历 (EMR) 为主动识别慢性疾病提供了有价值的数据.
- 使用非结构化的EMR数据需要先进的计算方法.
- 关节炎,慢性病,糖尿病,高血压和呼吸系统疾病等慢性疾病在初级保健中普遍存在.
研究的目的:
- 应用自然语言处理 (NLP) 和机器学习 (ML) 来使用结构化和非结构化EMR数据检测慢性疾病.
- 评估整合非结构化临床笔记对疾病检测模型性能的影响.
- 确定利用非结构化数据产生最显著改善的特定慢性疾病.
主要方法:
- 在449名老年人的EMR数据上使用NLP和ML开发了一个分析管道.
- 预处理的非结构化临床笔记和应用的隐性迪里克莱特分配 (LDA) 主题建模.
- 雇员监督的ML模型 (规范化后勤回归,SVM,ANN) 具有类加权学习和SMOTE来处理数据不平衡.
主要成果:
- 整合非结构化临床笔记显著改善了某些条件的模型性能.
- 关节炎检测 (SVM) 的ROC曲线下的面积从0.724增加到0.841.
- 呼吸道疾病检测 (ANN) 的ROC曲线下的面积从0.733增加到0.890.
- 糖尿病,高血压和慢性病 (CKD) 的情况有所改善.
结论:
- 利用非结构化的EMR数据,特别是临床笔记,可以提高初级保健中慢性疾病检测的准确性.
- 结合非结构化数据的性能收益因特定健康状况而有所不同.
- 这种计算方法有望改善初级保健机构的积极疾病识别和管理.
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