对于脏超声分析的自然语言处理:将成像报告与慢性脏病诊断相关联
Chenlu Wang1, Ritwik Banerjee1, Harry Kuperstein2
1Department of Computer Science, Stony Brook University, NY, USA.
Renal failure
|August 5, 2025
概括
自然语言处理 (NLP) 在脏超声波中准确识别了慢性病 (CKD) 的指标. 增加的回声性和小的大小是CKD的强有力的预测因素,使早期检测成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
背景情况:
- 慢性病 (CKD) 诊断通常依赖于解释非结构化的临床数据.
- 脏超声波报告为CKD检测提供了有价值的信息,但通常是手动分析.
- 使用自然语言处理 (NLP) 来用于CKD特征识别的这些报告的自动分析是一个尚未探索的领域.
研究的目的:
- 研究NLP技术在从脏超声波报告中提取CKD相关特征的有效性.
- 开发和验证NLP模型,以识别CKD的关键指标,如的回声性和大小.
- 评估NLP识别特征与CKD存在之间的关联.
主要方法:
- 一个单一中心的试点研究分析了使用NLP的1,068个脏超声波报告.
- 两种NLP方法 (词级和句子级) 用于分类的回声性 (正常与增加).
- 如果长度低于10个百分点,则被归类为"小";科医生建立了一个参考标准.
主要成果:
- 与句子级模型相比,单词级NLP模型在分类增加的回生性方面表现出更高的准确性.
- 双边增加的回声性被确定为CKD最强的预测因素 (OR=7.642,p<0.0001),其次是双边小脏 (OR=4.981,p=0.008).
- 患有CKD和双边增强的回声性的人显示功能显著降低.
结论:
- 先进的NLP模型可以有效地从超声报告中提取CKD相关的特征.
- NLP为早期CKD检测和风险分层提供了一个可扩展的解决方案.
- 建议在各种医疗保健系统中进一步验证这些NLP模型.
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