基准测试基于变压器的模型,用于在临床记录中识别健康的社会决定因素
Xiaoyu Wang1, Dipankar Gupta2, Michael Killian3
1Department of Statistics Florida State University Tallahassee, FL, USA.
概括
这项研究对从临床笔记中提取健康的社会决定因素 (SDoH) 的变压器模型进行了基准测试. 生物临床BERT取得了最佳表现,突出了其在医疗保健中改进机器学习模型的潜力.
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.
- 公共卫生 公共卫生
背景情况:
- 电子健康记录 (EHR) 对健康结果预测模型至关重要.
- 基于EHR的模型往往缺乏健康的社会决定因素 (SDoH) 数据,导致偏见和低估可预防的死亡.
- SDoH信息经常嵌入非结构化的临床笔记中.
研究的目的:
- 为了对七个预训练过的变压器模型在临床笔记中识别SDoH术语的性能进行基准测试.
- 评估自然语言处理 (NLP) 技术对SDoH信息提取的有效性.
- 确定最佳模型,以SDoH增强EHR数据,以提高预测准确度.
主要方法:
- 利用先前注释的MIMIC-III临床笔记库用于SDoH术语识别.
- 他们对七种基于变压器的模型进行了基准测试:BERT,ALBERT,BioBERT,BioClinicalBERT,RoBERTa,ELECTRA,以及RoBERTa-MIMIC-Trial.
- 根据严格和宽松的注释标准,使用F-1分数评估模型性能.
主要成果:
- 根据两个评估标准,BioClinicalBERT表现出卓越的性能,获得最高的F-1分数 (0.911和0.923).
- 所有基准变压器模型在识别SDoH术语方面都表现出不同程度的成功.
- 该研究证实了使用先进的NLP模型从临床文本中提取SDoH的可行性.
结论:
- 基于变压器的模型,特别是BioClinicalBERT,在准确地从临床笔记中提取SDoH信息方面显示出重大前景.
- 将提取的SDoH数据集成到EHR中可以帮助缓解机器学习模型中的偏差.
- 这种方法有可能提高健康结果预测的准确性和公平性.
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