一个智能医疗保健系统用于罕见疾病诊断,利用基于知识导向的多式联络变压器框架的电子健康记录
Ahed Abugabah1, Prashant Kumar Shukla2, Piyush Kumar Shukla3
1College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates.
BioData mining
|October 7, 2025
概括
诊断罕见疾病是一项挑战. 这项研究介绍了一个智能多式联络医疗保健框架,使用人工智能整合电子健康记录,基因组数据和医学图像,以更准确和及时地检测罕见疾病.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 基因组学和生物信息学
背景情况:
- 罕见疾病影响全球数以百万计的人,由于症状多样化和数据分散,造成诊断挑战.
- 现有的诊断工具难以整合多式联运数据,导致诊断延迟或不正确.
研究的目的:
- 开发一个智能多式联通医疗保健框架,以改善罕见病检测.
- 整合电子健康记录 (EHR),基因组序列和医学成像以提高诊断准确度.
主要方法:
- 使用Swin变压器从X光扫描中提取视觉特征.
- 雇佣了Med-BERT和Transformer-XL用于EHR叙事分析和图形神经网络 (GNN) 进行基因组序列分析.
- 整合了一个以知识为导向的对比学习 (KGCL) 机制与Orphanet罕见疾病本体学,并使用破子优化算法 (NOA) 进行了优化.
主要成果:
- 拟议的框架在早期罕见疾病诊断方面显著超过了最先进的多式联络基线.
- 在MIMIC-IV,ClinVar和CheXpert数据集中证明了诊断能力的提高准确性和稳定性.
- 展示了整合人工智能模型在罕见疾病检测中的多式联络数据融合的有效性.
结论:
- 开发的框架通过有效整合各种生物医学数据,为罕见疾病诊断提供了一种新的方法.
- 突出了人工智能驱动的多式联络分析在做出可解释,准确和临床适用的医疗保健决策方面的潜力.
- 通过综合数据分析,为早期发现和管理罕见疾病的进步铺平了道路.
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