一个通用的机器学习模型用于识别使用ICD代码的先天性心脏缺陷 (CHD)
Haoming Shi1,2, Wendy M Book3,4, Lindsey C Ivey4
1Department of Biomedical Engineering, Georgia Institute Technology, Atlanta, Georgia, USA.
Birth defects research
|January 31, 2025
概括
机器学习 (ML) 显著提高了先天性心脏缺陷 (CHD) 识别准确度,通过减少虚假阳性,当在国际疾病分类 (ICD) 代码选择后应用时. 这增强了对真实阳性冠状动脉疾病病例的监测.
科学领域:
- 医疗信息学 医疗信息学
- 心脏病学 心脏病学
- 机器学习 机器学习
背景情况:
- 国际疾病分类 (ICD) 代码对于识别先天性心脏缺陷 (CHD) 病例的错误阳性率很高.
- 提高CHD识别的准确性对于有效的公共卫生监测至关重要.
研究的目的:
- 评估机器学习 (ML) 算法的有效性,以提高心血管疾病病例识别的准确性.
- 评估ML是否可以改进传统的ICD代码为基础的病例选择.
主要方法:
- 将传统的ML方法应用于四个遭遇级数据集 (2010-2019) 对3334名经过验证的CHD诊断和ICD代码的患者.
- 使用5倍交叉验证方法来识别CHD分类的关键特征.
- 探索了各种培训和测试组合,以优化CHD分类准确性.
主要成果:
- 最初的CHD ICD阳性预测值 (PPVs) 根据地点有很大的差异 (53.2%84.0%).
- 在组合数据集中,ML算法实现了95%的高PPV,在优先考虑PPV时,假负率 (FN) 为33%.
- XGBoost有效地将2105临床分类软件 (CCS) 的功能减少到137个,区分真阳性心血管疾病病例与虚假阳性病例.
结论:
- 在ICD代码选择后集成ML算法大大提高了识别真阳性CHD病例的准确性.
- ML提供了一种有希望的方法,通过减轻ICD代码的局限性来完善CHD监测.
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