PetBERT:自动化ICD-11综合征疾病编码,用于在第一意见中检测疫情的兽医电子健康记录
Sean Farrell1, Charlotte Appleton2, Peter-John Mäntylä Noble3
1Department of Computer Science, Durham University, Durham, UK. sean.farrell2@durham.ac.uk.
Scientific reports
|October 21, 2023
概括
一个新的AI模型,PetBERT-ICD,分析兽医记录,以比人类临床医生更早地检测动物疾病. 这一进步旨在改善伴侣动物健康监测和公共卫生结果.
科学领域:
- 兽医流行病学 兽医流行病学
- 动物健康中的人工智能
- 公共卫生监督是对公共卫生的监督.
背景情况:
- 有效的公共卫生监测依赖于及时监测疾病信号,但伴侣动物监测框架缺乏.
- 兽医电子健康记录 (EHR) 提供了早期疾病检测的潜力,但受到自由文本数据的限制,阻碍了国家统计.
- 目前对伴侣动物的监测方法不足以对疾病发生产生动态反应.
研究的目的:
- 开发和评估PetBERT-ICD,这是一个用于兽医临床电子健康记录的自动编码的大型语言模型.
- 评估PetBERT-ICD在识别疾病爆发和改善兽医监测方面的能力.
- 加强伴侣动物的早期疾病检测,以改善公共卫生结果.
主要方法:
- 训练有素的PetBERT,一个大型语言模型,来自510万名英国兽医的5亿多个单词.
- 进一步培训PetBERT成为PetBERT-ICD,这是一个多标签分类器,用于使用国际疾病分类11 (ICD-11) 框架进行自动编码.
- 评估了PetBERT-ICD在疾病编码和疫情检测方面的表现,与临床医生分配的标签相比.
主要成果:
- 在20个疾病编码中,PetBERT-ICD实现了超过83%的F1分数,并提供了最小的注释.
- 该模型在识别疾病爆发方面表现出优异的性能,与当前临床医生指定的临床诊所标签策略相比.
- 皮特伯特-ICD比现有方法提前三周发现疾病爆发.
结论:
- 物BERT-ICD提供了一种强大的工具,用于兽医电子健康记录的自动编码,大大提高了伴侣动物的疾病监测.
- 该模型能够比人类临床医生更早地检测疾病爆发的能力,有望改善动物健康和公共健康.
- 使用人工智能的自动化EHR编码可以克服自由文本数据的局限性,使国家兽医统计数据更强大.
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