什么样的变压器模型用于ICD-10代码分类任务
Mariem Mansour1, Fatma Yilmaz1, Marko Miletic1
1Bern University of Applied Sciences, Switzerland.
Studies in health technology and informatics
|August 23, 2024
概括
变压器模型对简化复杂的国际疾病分类 (ICD) -10编码有希望. 增加上下文长度可以提高准确性,在分析电子健康记录时,编码器和编码器解码器架构之间的性能差异最小.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 国际疾病分类 (ICD) -10编码是复杂而昂贵的.
- 自然语言处理 (NLP) 可以分析非结构化的电子健康记录 (EHR),以帮助诊断编码.
研究的目的:
- 为了评估ICD-10分类的变压器模型.
- 为此任务比较编码器和编码器解码器架构.
主要方法:
- 使用了来自密集护理医疗信息中心 (MIMIC) -IV数据集的临床出院摘要.
- 适应预先训练的变压器模型,包括BioBERT,临床BERT,临床Longformer和临床BigBird.
- 应用特定的预处理技术以优化模型性能.
主要成果:
- 较长的上下文长度显著提高了编码准确性.
- 编码器和编码器解码器模型之间的性能差异是可以忽略不计的.
- 经过调整的变压器模型证明适合ICD-10分类.
结论:
- 变压器模型是有效的自动化ICD-10编码.
- 语境长度是提高基于NLP的医学编码准确性的关键因素.
- 编码器和编码器解码器变压器架构都对此任务具有相似的性能.
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