深度学习用于职业识别和知识发现在风湿病学临床笔记
Alfredo Madrid-García1, Inés Pérez-Sancristobal2, Leticia Leon2
1Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Prof. Martin Lagos s/n, 28040, Madrid, Spain. fredymad@msn.com.
在电子健康记录 (EHR) 中收集职业数据对于了解健康至关重要. 这项研究表明,自动化方法可以识别类型的职业在类风湿病学EHRs,将它们与特定的条件和改善患者护理.
科学领域:
- 类风湿病学 类风湿病学
- 数字健康数字健康
- 职业医学 职业医学是专业的.
背景情况:
- 职业数据是健康的关键社会决定因素,影响医疗保健.
- 电子健康记录 (EHR) 往往缺乏结构化的职业信息.
- 了解职业在风湿病学中的作用对于诊断和治疗至关重要.
研究的目的:
- 评估在风湿病学临床叙述中收集和使用职业数据的情况.
- 确定影响电子健康记录中职业数据收集的因素.
- 分析患者职业与诊断之间的关联.
主要方法:
- 一个预先训练好的西班牙语语言模型在生物医学文本上进行了微调.
- 该模型在35,586名类风湿患者的EHR中提及了所识别的职业.
- 职业数据使用欧洲技能,能力,资格和职业 (ESCO) 分类进行了规范化.
主要成果:
- 自动化模型获得了0.73的F1得分,识别了10%的患者的职业.
- 机械病理与较高的职业数据收集率有关.
- 特定的职业与不同的类风湿性疾病有关,包括自身免疫性疾病.
结论:
- 在EHR中自动识别职业是可行的,对风湿病学有价值的.
- 职业数据是风湿性疾病中健康的重要社会决定因素.
- 整合职业数据可以加强有针对性的预防和治疗策略.
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