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实体学引导的机器学习优于心脏超声波的零射击基础模型文本报告

Suganya Subramaniam¹, Sara Rizvi¹, Ramya Ramesh²

¹University of California, San Francisco, 521 Parnassus Avenue Rm 6222, San Francisco, CA, 94143, USA.

Scientific reports

|February 14, 2025

概括

统计机器学习有效地将心脏超声波文本映射到本体学,在详细分析方面表现优于大型语言模型. 这种方法增强了心血管研究中的大数据洞察力.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
自然语言处理自然语言处理.

背景情况:

心脏超声波报告包含有价值的数据,但在各机构的结构上有所不同.
分析这种非结构化文本具有挑战性,阻碍了大数据研究和质量改进.
自然语言处理 (NLP) 提供了从临床文本中提取见解的方法.

研究的目的:

评估NLP技术用于将心脏超声波文本映射到三级层次的层次结构本体学.
将统计机器学习 (EchoMap) 与大型语言模型 (GPT) 的性能进行比较.

主要方法:

利用来自24个机构的8个数据集进行培训和验证.
使用EchoMap (统计机器学习) 和零射击GPT (大型语言模型).
将NLP模型的输出与临床医生得分的基本事实进行比较.

主要成果:

EchoMap实现了高精度:98% (第一级),93% (1-2级) 和79% (1-3级).
在不同的数据集和概括能力上,EchoMap表现出一致的性能.
在更深层次的本体学层次 (2和3) 上,EchoMap的表现优于GPT.

结论:

统计机器学习,以EchoMap为例,准确地将心脏超声波文本映射到结构化的本体学.
这种方法即使在报告中存在机构差异的情况下也有效,并且可能是专门数据集的理想选择.

关键词:

心声图报告心声图报告大型语言模型.机器学习是机器学习.自然语言处理自然语言处理.存在论 (Ontology) 是一种存在论.

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