开发CAM-BERT:通过使用自然语言处理在住院的老年人中加强妄想查
Cesar Gomes Miguel1, José Adenaldo Santos Bittencourt1, Leonardo Daniel Tavares1
1Hospital Israelita Albert Einstein, Sao Paulo, Brazil.
Computers in biology and medicine
|July 17, 2025
概括
这项研究开发了一种使用自然语言处理 (NLP) 来检测巴西葡萄牙语临床笔记中的妄想症状的深度学习模型,显著提高了电子健康记录 (EHR) 的识别准确性. 人工智能模型有助于识别痴呆症,这是老年人常见的疾病.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理 (NLP) 用于临床文本分析
- 老年医学和 Delirium 检测
背景情况:
- 痴呆症在住院的老年人中很普遍,往往导致不良结果.
- 由于临床和系统性挑战, Delirium 的诊断不足是常见的.
- 需要改进的方法来识别电子健康记录 (EHR) 中的妄想症状.
研究的目的:
- 开发和评估一个深度学习NLP模型用于妄想症状识别.
- 训练模型使用来自EHR的巴西葡萄牙语临床笔记.
- 为了促进住院老年人痴呆症的检测和识别.
主要方法:
- 从500次老年人住院治疗中提取并注释的临床笔记.
- 使用BERTimbau深度学习模型和随机森林方法进行比较.
- 开发了CAM-BERT框架,以使检测到的症状与混评估方法 (CAM) 标准保持一致.
主要成果:
- 伯廷博模型取得了77%的F1宏积分,高于基线的39%.
- 高F1分数 (约90%) 获得了识别混乱和无组织思维.
- 在CAM-BERT框架中,通过专家审查,F1宏分数达到83%的良好一致性 (科恩的卡帕=0.72).
结论:
- 人工智能,特别是NLP,显示出支持妄想识别的巨大潜力.
- 开发的模型在非英语临床环境中有效,使用巴西葡萄牙语笔记.
- 为了更广泛的适用性,建议在不同的医疗保健环境中进行进一步的验证.
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