一种用于国家医疗数据环境中的隐私保护去重的晚融合多模态AI框架
A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments
AI总结：
本文提出一种多模态AI框架，通过语义嵌入、行为模式和设备元数据的晚融合方法，实现隐私保护下的去重，提升公共健康分析的可靠性。
摘要：
重复记录在客户关系管理（CRM）和医疗领域造成重大挑战，常导致分析不准确、用户体验受损和合规风险。传统去重方法依赖直接标识符如姓名、电子邮件或社会保障号（SSNs），在严格隐私法规如GDPR和HIPAA下效果不佳。本文提出一种新颖、可扩展的多模态AI框架，通过语义嵌入（基于文本字段如姓名、城市使用预训练的DistilBERT模型）、行为模式（从用户登录时间戳提取）和设备元数据（通过分类嵌入编码）三种不同模态，采用晚融合方法结合DBSCAN算法进行聚类。该模型在合成CRM数据集上评估，证明了其在数据变化和噪声下的有效性，实现了良好的F1分数。此方法提供了一种隐私合规的实体解析解决方案，支持安全的数字基础设施，提升公共健康分析的可靠性，并促进政府和企业环境中的伦理AI应用。
英文摘要：
Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails, or Social Security Numbers (SSNs), making them ineffective under strict privacy regulations like GDPR and HIPAA, where such personally identifiable information (PII) is restricted or masked. In this research, I propose a novel, scalable, multimodal AI framework for detecting duplicates without depending on sensitive information. This system leverages three distinct modalities: semantic embeddings derived from textual fields (names, cities) using pre-trained DistilBERT models, behavioral patterns extracted from user login timestamps, and device metadata encoded through categorical embeddings. These heterogeneous modalities are combined using a late fusion approach and clustered via DBSCAN, an unsupervised density-based algorithm. This proposed model is evaluated against a traditional string-matching baseline on a synthetic CRM dataset specifically designed to reflect privacy-preserving constraints. The multimodal framework demonstrated good performance, achieving a good F1-score by effectively identifying duplicates despite variations and noise inherent in the data. This approach offers a privacy-compliant solution to entity resolution and supports secure digital infrastructure, enhances the reliability of public health analytics, and promotes ethical AI adoption across government and enterprise settings. It is well-suited for integration into national health data modernization efforts, aligning with broader goals of privacy-first innovation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
