使用临床叙事意识预先训练的语言模型来预测急诊室患者态度和非计划的回访
Tzu-Ying Chen1, Ting-Yun Huang2, Yung-Chun Chang3
1Graduate Institute of Data Science, Taipei Medical University, Taipei City, Taiwan.
Journal of biomedical informatics
|May 21, 2024
概括
一个BlueBERT自然语言处理模型准确地预测了急诊室患者的态度,并减少了再访. 这种人工智能工具通过分析临床叙述来增强患者护理和医院资源管理.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理 (NLP) 是一种自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 紧急部门 (ED) 过度拥挤影响医疗保健服务和患者的结果.
- 有效的患者处置管理对于优化ED操作至关重要.
- 预测模型可以帮助在ED中及时分配护理和资源.
研究的目的:
- 开发和评估一种自然语言处理 (NLP) 模型,用于预测ED环境中的患者倾向和意外再接收.
- 利用BlueBERT,一个生物医学领域特定的语言模型,提高预测准确性.
- 评估数据预处理技术 (包括翻译和文本化) 对模型性能的影响.
主要方法:
- 利用了台北医科大学 - 舒安霍医院的约25万个ED访问记录.
- 使用BlueBERT开发了一个预测模型,这是一个专门用于生物医学文本的预训语言模型.
- 采用数据预处理,包括中英临床叙事翻译和数字数据文本化.
主要成果:
- 蓝伯特模型在预测患者情绪方面取得了很高的准确性 (AUROC = 0.9014).
- 该模型在预测72小时未计划的回访时表现出显著的能力 (AUROC = 0.6475).
- 数据翻译和文本化显著提高了NLP模型的预测性能.
结论:
- 与之前的模型相比,基于BlueBERT的NLP模型在预测患者情绪方面表现优越.
- 这种方法为改善急诊室的临床实践和运营效率提供了一个有希望的工具.
- 这些发现强调了先进的NLP技术在解决ED过度拥挤和提高患者护理方面的潜力.
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