诊断代码与临床笔记的比较,用于对糖尿病视网膜病变患者进行分类
Sean Yonamine1,2, Chu Jian Ma1, Rolake O Alabi1
1Department of Ophthalmology, University of California, San Francisco, California.
Ophthalmology science
|September 10, 2024
概括
一个分析临床记录的自然语言处理 (NLP) 算法改善了多发性糖尿病视网膜病变 (PDR) 的分类,而不是传统的ICD代码. 这证明了NLP.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 计算健康 计算健康
背景情况:
- 电子健康记录 (EHR) 包含广泛的临床数据,对研究有价值.
- 从EHR中对患者队列的自动分类可以提高研究效率和准确性.
- 糖尿病视网膜病变 (DR) 的分类,特别是增殖 (PDR) 和非增殖 (NPDR) 阶段,对于患者管理和研究至关重要.
研究的目的:
- 评估基于规则的自然语言处理 (NLP) 算法是否使用临床笔记优于国际疾病分类 (ICD-9/10) 代码来分类PDR和NPDR严重程度.
- 评估NLP在确定研究患者队伍的准确性和效率.
主要方法:
- 一项横截面研究使用了来自2366名糖尿病患者的未识别的EHR数据.
- 一组306名患者接受了眼科医生图表审查,以建立PDR和NPDR分类的黄金标准.
- 开发了一种基于规则的NLP算法,以识别临床笔记中的PDR和NPDR提及,并与ICD代码和黄金标准进行比较.
主要成果:
- 在PDR分类方面,NLP算法显著超过ICD代码,显示出更高的灵敏度 (90.5%与68.4%) 和更高的F1得分 (0.941与0.794).
- 在PDR分类方面,ICD-10代码的表现优于ICD-9代码 (F1得分为0.836比0.596).
- 该NLP算法表现出与NPDR严重程度分类的ICD代码相似的性能,尽管注意到样本大小的限制.
结论:
- 基于规则的NLP算法应用于临床笔记,提供了一个比传统ICD代码更准确和更有效的方法来分类PDR.
- 这种方法具有显著的潜力,可以在涉及糖尿病视网膜病变的临床研究中改善队列选择.
- 进一步的研究可能会探索NLP用于使用更大的数据集进行NPDR分类.
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