摘要
随着信息技术的迅速进步,医疗数据的种类和数量不断增加,涵盖了电子病历、医学影像、基因组学、实验室检测结果等多个方面,多样化的数据来源使得数据融合与应用变得极为复杂,但同时也为智能医疗诊断系统的构建提供了丰富的基础。本文提出了一种基于多模态深度学习的智能医疗诊断系统,通过融合电子健康记录(EHR)、医学影像、基因组数据等多源异构数据,构建了一个高效的医疗诊断模型。实验结果表明,该模型在多种疾病的诊断准确率上优于传统方法,显示了异构数据融合在智能医疗中的重要应用价值。
With the rapid advancement of information technology,the types and quantities of medical data continue to increase,covering multiple aspects such as electronic medical records,medical imaging,genomics,laboratory test results,etc.The diverse sources of data make data fusion and application extremely complex,but also provide a rich foundation for the construction of intelligent medical diagnostic systems.This article proposes an intelligent medical diagnosis system based on multimodal deep learning,which integrates multiple heterogeneous data sources such as electronic health records(EHR),medical imaging,and genomic data to construct an efficient medical diagnosis model.The experimental results show that the model outperforms traditional methods in the diagnosis accuracy of various diseases,demonstrating the important application value of heterogeneous data fusion in intelligent healthcare.
作者
孙文业
汪春亮
SUN Wenye;WANG Chunliang(Suzhou University Affiliated Second Hospital,Suzhou Jiangsu 215000)
出处
《软件》
2025年第6期119-121,共3页
Software
关键词
智能医疗诊断系统
多模态深度学习
异构数据
模型研究
intelligent medical diagnostic system
multimodal deep learning
heterogeneous data
modeling
