Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data

Background Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative. Objectives To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs). Methods Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm. Results Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV. Conclusion The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.

Intra-database Validation of Case-identifying Algorithms Using Reconstituted Electronic Health Records From Healthcare Claims Data

Background: Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative.Objectives: To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs).Methods: Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conduct to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each diseases, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm. Results: Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV and for mCRPC identification, a 97% PPV and 99% NPV. Conclusion: The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.

Development of an information system of Oracle programs in blockchain management systems

Oracles programs accept information from various sources, transform it, and transmit it to smart contracts. They can also accept data from a smart contract and transmit it to an external data source. Ensuring the security, validity and integrity of the supplied data determines the success of the blockchain system, therefore, the research topic is relevant. The purpose of this article is to identify practically important features of Oracle programs and develop a version of the information system architecture for Oracles programs that meets the necessary requirements. The authors were faced with the task of investigating all the vulnerabilities associated with the use of Oracle programs and developing an optimal architectural solution. In the course of research, methods of reviewing scientific literature on the subject of research, collecting, structuring and analyzing the information received, and methods of choosing solutions were used. As a result of the research, the concept of an intelligent system for transferring external data to a blockchain management system is proposed and the optimal architecture of this intelligent system is developed. This solution is aimed at improving the security of using Oracle programs for blockchain management systems, especially blockchain management systems for industrial Internet of things applications. The solution can be used by developers of distributed registry systems to effectively launch and implement projects.

Big Data Framework for Auditing Process

An audit of financial report is a review of the organization financial statement that carried out by the independent and professional in their field which is the Auditor. The Big Data methodology offered a different approach compared to the current audit procedure, which mostly using manual process. Big Data equipped with learning capabilities and automation process in order to achieve better and faster result. Another advantage of using Big Data methodology is to provide comprehensive and multi-dimensional view of the problem. This paper provides a framework to incorporated auditing process and Big Data approach, specifically by mapping the internal and external data source to one of audit process stage, which is risk assessment process.

Integration of the Java Image Science Toolkit with E-Science Platform

Medical image analyses rely on diverse software packages assembled into a “pipeline”. The Java Image Science Toolkit (JIST) has served as a standalone plugin into the Medical Image Processing Analysis and Visualization (MIPAV). We addressed shortcomings that previously prevented deeper integration of JIST with other E-science platforms. First, we developed an interface for integrating externally compiled packages (similar to the interfaces in NiPy) such that the application can become a “draggable module” in the module tree. This allows for connection of inputs and outputs to other JIST modules while maintaining external processing and monitoring. Second, we develop an integration interface with the Neuroimaging Informatics Tools and Resources Clearinghouse Cloud Environment (NITRC-CE). User can launch and terminate pre-configured nodes to utilize computational resources of the Amazon cloud. Finally, we define a new external data source, which can connect to the eXtensible Neuroimaging Archive Toolkit (XNAT) to query and retrieve remote data using XNAT’s REST API. Specifically, we define dataflow for files that can readily be converted into volumes and collections of volumes to interface with any JIST module that expects volumetric image data as an input. Users now have the ability to run their pipelines from a well-defined external data source and no longer are required to already have data on the disk. With these upgrades we have extended JIST’s capabilities outside of complied java source code and enhanced capabilities to seamlessly interface with E-science platforms.

On Handling the Evolution of External Data Sources in a Data Warehouse Architecture

A data warehouse architecture (DWA) has been developed for the purpose of integrating data from multiple heterogeneous, distributed, and autonomous external data sources (EDSs) as well as for providing means for advanced analysis of integrated data. The major components of this architecture include: an external data source (EDS) layer, and extraction-transformation-loading (ETL) layer, a data warehouse (DW) layer, and an on-line analytical processing (OLAP) layer. Methods of designing a DWA, research developments, and most of the commercially available DW technologies tacitly assumed that a DWA is static. In practice, however, a DWA requires changes among others as the result of the evolution of EDSs, changes of the real world represented in a DW, and new user requirements. Changes in the structures of EDSs impact the ETL, DW, and OLAP layers. Since such changes are frequent, developing a technology for handling them automatically or semi-automatically in a DWA is of high practical importance. This chapter discusses challenges in designing, building, and managing a DWA that supports the evolution of structures of EDSs, evolution of an ETL layer, and evolution of a DW. The challenges and their solutions presented here are based on an experience of building a prototype Evolving-ETL and a prototype Multiversion Data Warehouse (MVDW). In details, this chapter presents the following issues: the concept of the MVDW, an approach to querying the MVDW, an approach to handling the evolution of an ETL layer, a technique for sharing data between multiple DW versions, and two index structures for the MVDW.