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开发和评价一个可计算的表型,用于正常张力玻璃眼

Fountane Chan¹, Wei-Chun Lin¹, Alan Tang²

¹Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.

Ophthalmology science

|July 29, 2025

概括

使用电子健康记录 (EHR) 开发正常张力玻璃眼 (NTG) 的可计算表型显著改善了疾病的识别. 结合自然语言处理 (NLP) 的表型表现出比传统的ICD代码单独表现出更高的性能.

科学领域:

眼科医生眼科眼科
医疗信息学医疗信息学
健康数据科学数据科学

背景情况:

正常张力玻璃眼 (NTG) 是玻璃眼的一种亚型,需要准确识别才能有效管理.
电子健康记录 (EHR) 包含大量的患者数据,可以用于疾病表型.
开发可计算的表型对于提高疾病检测和从EHR数据中进行研究至关重要.

研究的目的:

使用EHR数据开发和验证正常张力玻璃眼 (NTG) 的可计算表型.
在大型数据集中提高识别NTG患者的准确性和效率.
为了比较不同的表型化方法的性能,包括使用ICD代码,结构化数据和自然语言处理 (NLP) 的方法.

主要方法:

进行了一项回顾性队列研究,使用了来自1851名年龄≥40岁的绿眼病患者的未识别的EHR数据.
开发了四种基于规则的NTG可计算的表型: 1) 仅使用ICD代码, 2) 结构化IOP和药物数据, 3) 结构化IOP数据, 4) 结构化数据与NLP结合用于笔记.
现象类型被内部和外部验证,使用图表审查进行金标准诊断.

主要成果:

图表审查证实NTG在30%的患者中,只有7%的患者具有NTG ICD代码.
基于ICD代码的表型 (表型1) 显示敏感度低 (24.1%),但特异性高 (97%).
结合结构化数据 (Phenotypes 2和3) 和NLP (Phenotype 4) 的表型显著提高了性能,其中Phenotype 4获得了最高的F1得分 (77.4%),灵敏度 (82.8%) 和准确度 (85.5%).
与表现型1相比,表现型2-4增加了NTG病例检测的四倍.

结论:

使用NLP的NTG可计算的表型在识别来自EHR的患者方面取得了最佳表现.
包含结构化数据的表型优于仅依赖于缺乏敏感性的ICD代码的表型.
这些发现可以指导其他疾病亚型的可计算表型的发展.

关键词:

可计算的现象型.欧洲人权理事会欧洲人权理事会其他 NTG NTG 自然语言处理自然语言处理.正常张力玻璃眼 (Glaucoma) 是一种正常张力玻璃眼.

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