CURENet:结合统一的表示方式,有效地预测慢性疾病.
Cong-Tinh Dao1,2, Nguyen Minh Thao Phan1,2, Jun-En Ding3
1National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Health information science and systems
|December 1, 2025
概括
新的多式模式CURENet有效地整合了各种电子健康记录 (EHR) 数据,包括临床笔记和实验室测试,以改善慢性疾病的预测. 这种方法通过捕捉复杂的数据相互作用来增强临床决策和患者的结果.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
- 临床数据科学 临床数据科学
背景情况:
- 电子健康记录 (EHR) 包含各种数据类型 (笔记,实验室,访问) 对于患者健康评估至关重要.
- 当前的预测模型往往无法有效地整合多式联络电子健康记录数据,从而限制了预测的准确性.
- 捕捉跨数据模式的时间模式和相互作用对于强大的临床预测至关重要.
研究的目的:
- 开发和评估CURENet,一种使用综合EHR数据预测慢性疾病的多式模式.
- 解决现有模型在处理EHR数据中的复杂相互作用方面的局限性.
- 通过多式联运数据融合,提高慢性疾病预测的可靠性.
主要方法:
- CURENet集成了使用大型语言模型 (LLM) 和变压器编码器的非结构化临床笔记,实验室测试和时间序列访问数据.
- 临床医疗器械处理临床文本和文本实验室结果,而转换器分析患者纵向访问数据.
- 该模型在MIMIC-III和FEMH数据集上进行了评估,用于多标签慢性疾病预测.
主要成果:
- 在预测前10个慢性疾病方面,CURENet的准确率超过了94%.
- 该模型展示了捕获不同临床数据模式之间的复杂相互作用的能力.
- 在公共 (MIMIC-III) 和私人 (FEMH) 数据集上成功验证.
结论:
- 使用CURENet的多模式EHR数据集成显著提高了慢性疾病的预测.
- 该模型处理各种数据类型的能力提高了医疗保健中预测分析的可靠性.
- 研究结果表明,CURENet有可能促进临床决策并改善患者的治疗结果.
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