一个压缩的大型语言模型,嵌入ICD 10 CM描述的数据集
Michael J Kane1, Casey King2,3, Denise Esserman4
1Department of Biostatistics, School of Public Health, Yale University, New Haven, USA. michael.kane@yale.edu.
新的数据集以数字方式表示国际疾病分类第10版临床修改 (ICD-10-CM) 代码. 这些嵌入式捕捉关系和上下文,增强机器学习用于生物医学信息学研究.
科学领域:
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 国际疾病分类,第10版,临床修改 (ICD-10-CM) 代码对于医疗保健至关重要,但缺乏先进分析的固有数值表示.
- 在机器学习中利用ICD-10-CM代码的现有方法受到其离散性质和缺乏上下文信息的限制.
研究的目的:
- 为ICD-10-CM代码生成数字表示的新数据集.
- 通过捕捉语义关系和上下文,为机器学习模型创建信息输入功能.
- 通过使用易于获得的数据集,使生物医学信息学更先进的分析成为可能.
主要方法:
- 使用大型语言模型生成ICD-10-CM代码的描述嵌入.
- 通过自动编码器应用尺寸缩小来压缩嵌入式.
- 使用自动编码器和监督模型进行层次类别估计,验证了尺寸缩小.
主要成果:
- 成功地将ICD-10-CM代码嵌入的维度降低到10个维度.
- 保持了在缩小尺寸时高保真地复制原始嵌入的能力.
- 为用户可选择的要求提供多个压缩级别.
结论:
- 由ICD-10-CM代码生成的数值数据集显著提高了它们在生物医学信息学中的实用性.
- 这种方法通过提供上下文感知,缩小维度的功能来促进更先进的机器学习应用程序.
- 随时可用的数据集不需要额外的设置,促进更广泛的采用和研究.
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