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开发一种自然语言处理模型,用于提取脏活检病理诊断诊断

Shane A Bobart^1,2, Enshuo Hsu^3,4,5, Thomas Potter³

¹Division of Nephrology, Hypertension and Transplantation, Houston Methodist Hospital, Houston, TX.

Kidney medicine

|August 1, 2025

概括

这项研究开发了一种自然语言处理 (NLP) 模型,以准确地从自由文本报告中提取脏活检诊断. NLP系统表现出高性能,促进了研究和临床试验招聘.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
医疗信息学医疗信息学
计算语言学计算语言学

背景情况:

脏活检报告是非结构化的文本,需要手动抽象进行诊断.
自然语言处理 (NLP) 尚未得到广泛验证,用于提取脏活检诊断.

研究的目的:

开发和评估一个准确的NLP模型,从自由文本报告中提取脏活检诊断.
提高脏活检数据分析的效率和可扩展性.

主要方法:

利用PubMed从变压器 (BERT) 的双向编码器表示来开发NLP模型.
使用结构化查询语言 (SQL) 和Python处理了2,666名患者的3,042份脏活检报告.
将报告分为培训 (80%) 和测试 (20%) 集,用于模型开发和验证.

主要成果:

在所有诊断中,NLP模型实现了接受器操作曲线 (AUROC) 下的平均面积为0.95.
对于20个最常见的诊断,该模型实现了0.97的AUROC和0.72.7的F1得分.
在手动抽象中观察到高interrater可靠性 (Cohen kappa = 0.76).

结论:

从自由文本报告中提取脏活检诊断的准确和可扩展的NLP系统已经被证明.
该系统可以显著帮助流行病学研究和临床试验的患者鉴定.
NLP模型为推进病研究提供了一个有价值的工具.

关键词:

来自变压器的双向编码器表示自然语言处理自然语言处理.球体隆基尼弗里斯 (glomerulonephritis) 是一种发生在人体中的疾病.脏活组织检查机器学习是机器学习.

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