Multi-Source Medical Data Integration and Mining for Healthcare Services

• Main Authors: Qingguo Zhang, Bizhen Lian, Ping Cao, Yong Sang, Wanli Huang, Lianyong Qi
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Service recommendation; Internet of Health; locality-sensitive hashing; user privacy; data integration
• Online Access: https://ieeexplore.ieee.org/document/9194739/

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.