多模式数据混合融合和自然语言处理用于临床预测模型
Jiancheng Ye1, Jiarui Hai2, Jiacheng Song3
1Weill Cornell Medicine, New York, NY, USA.
概括
这项研究通过结合结构化的电子健康记录和非结构化的临床笔记,使用一种新的混合方法来增强临床预测模型. 这种方法显著提高了对患者伤害的预测准确度.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据融合技术 数据融合技术
背景情况:
- 临床预测模型通常依赖于有限的数据源,影响其准确性.
- 整合各种数据类型,如电子健康记录 (EHR) 和临床笔记,存在挑战.
- 现有的多模式融合方法可能无法完全捕获复杂的患者信息.
研究的目的:
- 开发和评估一种新的混合融合方法,以增强临床预测模型.
- 整合结构化的电子健康记录数据,非结构化的临床笔记和国家电子伤害监测系统 (NEISS) 数据.
- 通过多式联运数据融合,提高预测模型的准确性和稳定性.
主要方法:
- 提出了一个全面的框架,整合多式联运数据源.
- 开发了一种混合融合方法,采用预先训练的语言模型 (RoBERTa).
- 将非结构化的临床文本与结构化的EHR数据和NEISS数据融合在一起,以提供全面的患者代表性.
主要成果:
- 混合融合方法显著超过了传统的融合框架和单模式模型.
- 基于RoBERTa的混合融合系统实现了75.00%的准确性,用于Top 1伤害预测.
- 该系统实现了93.54%的准确性,对前三名伤害预测.
结论:
- 将NLP与多式联络数据融合相结合,可以提高临床预测模型的性能.
- 拟议的方法通过利用丰富的临床文本和结构化的EHR数据来提高准确性和稳定性.
- 这种方法在临床决策支持,个性化医学和基于证据的医疗保健方面具有潜在的应用.
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