With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.

中文翻译：

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医疗服务的多源医疗数据整合与挖掘


随着健康互联网（IoH）时代的到来，传统医疗或健康服务逐渐迁移到Web或互联网，并产生了大量与患者、医生、药品、医疗基础设施等相关的医疗数据。这些IoH数据的有效融合和分析对于科学的灾害诊断和医疗服务具有积极的意义。然而，IoH数据通常分布在不同部门并包含部分用户隐私。因此，有效地集成或挖掘敏感的车联网数据往往是一项具有挑战性的任务，在此过程中用户隐私不被泄露。为了克服上述困难，我们提出了一种新颖的多源医疗数据集成和挖掘解决方案，以提供更好的医疗服务，称为PDFM（无隐私数据融合和挖掘）。通过PDFM，我们可以高效且保护隐私的方式搜索相似的病历，从而为患者提供更好的医疗健康服务。在这项工作中制定并实施了一组实验来证明该提案的可行性。