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使用测试时间增强改进希伯来PET-CT病理学报告的严重程度分类

Seffi Cohen¹, Edo Lior¹, Moshe Bocher²

¹Department of Software and Information Systems Engineering, Ben Gurion University, Beer-Sheva, 8410501, Israel.

Journal of biomedical informatics

|December 15, 2023

概括

本研究引入了文本测试时间增大 (TTTA),以改善希伯来医疗报告中的癌症严重程度分类. TTTA通过增强文本数据来提高准确性,提高了15.18%的性能.

科学领域:

医疗信息学医疗信息学
自然语言处理自然语言处理.
计算语言学计算语言学

背景情况:

由于语言的复杂性和模糊性,希伯来医疗报告的分类是很困难的.
从诊断报告中准确地分类癌症的严重程度对于患者的护理至关重要.

研究的目的:

提议和评估文本测试时间增大 (TTTA) 以改善希伯来语PET-CT报告中癌症严重程度的分类.
增强分析形态丰富医学文本的模型的稳定性和语义提取能力.

主要方法:

在培训和测试阶段开发和应用文本测试时间增长 (TTTA).
生成和评估文本增强以改善信息检索和对希伯来医疗报告的模型理解.
利用了来自Ziv医院的大量手动标记的希伯来语PET-CT报告的数据集.

主要成果:

在没有增强的情况下,TTTA方法显著超过了基线模型的表现.
在癌症严重程度等级分类的PR-AUC中实现了15.18%的改善.
证明了医疗概念的增强提取和更好的分类准确性.

结论:

TTTA是一种有效的方法来分析复杂的希伯来医学文本,特别是用于癌症严重程度的分类.
该方法解决了对形态丰富的医学语言自动分析的局限性.
TTTA显示有潜力帮助医生进行癌症诊断和治疗计划.

关键词:

在此之前,NLPH是NLPH.在PET-CT中使用.报告-分类报告-分类这就是TTA TTA.

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