摘 要
随着信息技术的迅猛发展，智慧医疗已成为现代医疗体系转型的重要方向。多模态数据融合技术作为支撑智慧医疗的核心手段，能够有效整合来自不同来源和类型的医疗数据，提升诊断准确性与治疗效率。本文旨在系统探讨面向智慧医疗的多模态数据融合技术，分析其在临床场景中的应用潜力与挑战。研究基于对当前主流数据融合方法的梳理，结合深度学习与人工智能技术，提出一种具有上下文感知能力的融合框架，并引入跨模态特征对齐机制以增强模型表达能力。通过在多个真实医疗数据集上的实验验证，所提方法在疾病分类、病情预测等任务中表现出优于传统方法的准确率与鲁棒性。研究表明，合理设计的数据融合策略不仅能提升智能诊疗系统的性能，还能促进医疗资源的高效配置。本文的主要创新在于构建了一个适应性强、可扩展的多模态融合架构，并提出了针对医疗场景优化的信息加权机制。研究成果为推动智慧医疗系统向更高层次的智能化、个性化发展提供了理论支持与技术路径。
关键词：智慧医疗；多模态数据融合；深度学习；跨模态特征对齐；上下文感知
Abstract
With the rapid development of information technology, smart healthcare has become a crucial direction for the transformation of modern medical systems. As a core approach supporting smart healthcare, multimodal data fusion techniques can effectively integrate medical data from diverse sources and types, thereby enhancing diagnostic accuracy and treatment efficiency. This paper systematically explores multimodal data fusion technologies tailored for smart healthcare, analyzing their application potential and challenges in clinical settings. Based on a review of current mainstream data fusion methods and combined with deep learning and artificial intelligence technologies, this study proposes a context-aware fusion fr amework and introduces a cross-modal feature alignment mechanism to enhance model expressiveness. Experimental validation conducted on multiple real-world medical datasets demonstrates that the proposed method achieves superior accuracy and robustness compared to traditional approaches in tasks such as disease classification and condition prediction. The findings indicate that well-designed data fusion strategies not only improve the performance of intelligent diagnostic systems but also promote the efficient allocation of medical resources. The primary innovation of this work lies in the construction of an adaptable and scalable multimodal fusion architecture, along with the development of an information weighting mechanism specifically optimized for medical scenarios. The research outcomes provide theoretical support and technical pathways for advancing smart healthcare systems toward higher levels of intelligence and personalization.
Keywords: Smart Healthcare; Multimodal Data Fusion; Deep Learning; Cross-modal Feature Alignment; Context-aware
目 录
1绪论 1
1.1研究背景与智慧医疗发展趋势 1
1.2多模态数据融合的技术价值与挑战 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2智慧医疗中多模态数据的类型与特征分析 2
2.1医疗影像数据的获取与处理方式 2
2.2生理信号数据的采集与建模方法 2
2.3电子健康记录的数据结构与语义解析 3
2.4多源异构数据的标准化与预处理策略 3
3面向智慧医疗的多模态数据融合模型构建 4
3.1数据级融合方法及其适用性分析 4
3.2特征级融合中的表示学习技术 4
3.3决策级融合机制的设计与优化 5
3.4融合模型的可解释性与鲁棒性探讨 6
4多模态数据融合在典型医疗场景中的应用 6
4.1融合技术在疾病辅助诊断中的实践 6
4.2基于融合数据的个性化治疗方案生成 7
4.3多模态信息支持下的远程监护系统设计 7
4.4融合结果在临床决策支持中的评估 7
结 论 8
参考文献 9
致 谢 10
