Currently there is not a single trusted infrastructure used for the exchange and storage of medical data along the healthcare value chain and, thus, there is no platform used for monitoring patients’ traceability within the entire healthcare chain. This situation leads to difficult communication and increased procedural costs, and thus it limits healthcare players from developing a better understanding and know-how of patients’ traceability that could further boost innovation and development of the best-fitted health services. PatientDataChain blockchain-based technology is a novel approach, based on a decentralized healthcare infrastructure that incorporates a trust layer in the healthcare value chain. Our aim was to provide an integrated vision based on interoperability principles, that relies on the usage of specific sensors from various wearable devices, allowing us to collect specific data from patients’ medical records. Interconnecting different healthcare providers, the collected data is integrated into a unitary personal health records (PHR) system, where the patient is the owner of his/her data. The decentralized nature of PatientDataChain, based on blockchain technology, leveraged the proper context to create a novel and improved data-sharing and exchange system, which is secure, flexible, and reliable. This approach brings increased benefits to data confidentiality and privacy, while providing secure access to patient medical records. This paper presents the design, implementation, and experimental validation of our proposed system, called PatientDataChain. The original contributions of our paper include the definition of the concept of unifying the entire healthcare value chain, the design of the architectural model of the system, the development of the system components, as well as the validation through a proof of concept (PoC) conducted with a medical clinic from Bucharest, using a dataset of 100 patients and over 1000 transactions. The proof of concept demonstrated the feasibility of the model in integrating the personal health records from heterogeneous sources (healthcare systems and sensors) in a unified, decentralized PHR system, with enhanced data exchange among healthcare players.

No abstract

This paper focuses on the compression based clustering and aims to determine the most suitable combinations of algorithms for different clustering contexts (text, heterogeneous data, Web pages, metadata and so on) and establish whether using compression with traditional clustering methods leads to better performance. In this context, we propose an integrated cluster analysis test platform, called EasyClustering, which incorporates two subsystems: a clustering component and a cluster validity expert system, which automatically determines the quality of a clustering solution by computing the FScore value. The experimental results are focused on two main directions: determining the best approach for compression based clustering in terms of context, compression algorithms and clustering algorithms, and validating the functionality of the cluster analysis expert system for determining the quality of the clustering solutions. After conducting a set of 324 clustering tests, we concluded that compressing the input when using traditional clustering methods increases the quality of the clustering solutions, leading to results comparable to the NCD and the cluster analysis expert system proved 100% its accuracy so far, so we estimate that, even if some slight deviation should occur, it will be minimal.

No abstract

No abstract