创伤ICD双向编码器表示从变压器:自然语言处理算法提取损伤国际疾病分类,第10版诊断代码从自由文本
Jeff Choi1,2, Yifu Chen2, Alexander Sivura3
1Department of Surgery, Stanford University, Stanford, CA.
一个新的算法,创伤ICDBERT,准确地预测伤害国际疾病分类,第10版 (ICD-10) 代码从创伤笔记. 这种自然语言处理工具对实时临床应用有很大的希望.
科学领域:
- 医疗信息学医学信息学
- 自然语言处理自然语言处理.
- 创伤外科 手术 创伤外科
背景情况:
- 准确和及时的国际疾病分类,第10版 (ICD-10) 诊断代码对于临床环境中的伤害预测至关重要.
- 目前的自然语言处理 (NLP) 算法在准确预测伤害ICD-10代码方面存在局限性.
- ICD-10代码的实时可用性是其在临床实践中采用的重要障碍.
研究的目的:
- 开发和验证创伤ICDBERT,一种新的NLP算法,旨在预测受伤ICD-10诊断代码.
- 用创伤第三级调查笔记来评估TraumaICDBERT的表现.
- 为了比较TraumaICDBERT与现有的NLP工具的有效性.
主要方法:
- 创伤ICDBERT算法是使用2016年1月至2021年6月的成人创伤三级调查笔记开发和验证的.
- 创伤ICDBERT基于BioLinkBERT模型,这是一个来自变压器架构的双向编码器表示.
- 绩效评估包括将TraumaICDBERT与亚马逊网络服务Comprehend Medical进行比较.
主要成果:
- 该研究分析了3478份第三级调查笔记,其中包含15762个四个字符的伤害ICD-10诊断码.
- 与亚马逊网络服务Comprehend Medical相比,TraumaICDBERT在所有评估指标中表现出更好的表现.
- 每个三级调查笔记与平均3.8 (SD:2.9) 的创伤登记员提取的四个字符的伤害ICD-10诊断码有关.
结论:
- 创伤ICDBERT在预测创伤方面表现出强的初始表现,来自创伤第三级调查笔记的ICD-10诊断码.
- 该算法有可能在临床环境中增强下游预测工具的采用.
- 这一进步可以提高伤害编码和预测的效率和准确性.
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