症状-BERT:在EHR临床注释中提高癌症症状检测
Nahid Zeinali1, Alaa Albashayreh2, Weiguo Fan3
1Department of Computer Science and Informatics (N.Z.), University of Iowa, Iowa, USA.
Journal of pain and symptom management
|May 24, 2024
概括
一种新的Symptom-BERT模型,在临床数据上进行预训练,可以准确地检测电子健康记录中的癌症症状. 这种人工智能的进步改善了症状记录,以实现个性化的患者护理和更好的结果.
科学领域:
- 人工智能在医学中的应用
- 医疗保健的自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 从临床叙述中提取癌症症状数据对于个性化的症状预测和管理至关重要.
- 先进的语言模型可以显著提高在临床笔记中的症状数据的检测.
研究的目的:
- 开发和评估一种预训练的语言模型,用于从临床笔记中检测和提取癌症症状.
- 评估Symptom-BERT模型在识别各种癌症症状组中的性能.
主要方法:
- 在一百万个未标记的临床文件上预先训练了一种生物临床BERT模型.
- 对13个癌症症状组的1112条注释笔记进行了微调的症状-BERT.
- 通过使用ChatGPT-4生成的180个合成笔记进行外部验证.
主要成果:
- 症状-BERT实现了高的内部性能 (F1得分0.933,AUC0.929) 和强大的外部验证 (AUC0.834).
- 该模型在检测身体症状 (例如,) 时,与心理症状 (例如,焦虑) 相比,显示出更高的准确性.
结论:
- 针对特定领域数据的专业预训练显著提高了医疗应用的语言模型性能.
- 症状-BERT模型代表了人工智能的重大进步,用于以患者为中心的癌症症状检测和管理.
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