Versatile: a scalable CORBA-based system for integrating distributed data

An often key component to coordinating surveillance activities across distributed networks is the design and implementation of a common data model (CDM). The purpose of this study was to evaluate two drug safety surveillance CDMs from an ecosystem perspective to better understand how differences in CDMs and analytic tools affect usability and interpretation of results. Humana claims data from 2007 to 2012 were mapped to Observational Medical Outcomes Partnership (OMOP) and Mini-Sentinel CDMs. Data were described and compared at the patient level by source code and mapped concepts. Study cohort construction and effect estimates were also compared using two different analytical methods--one based on a new user design implementing a high-dimensional propensity score (HDPS) algorithm and the other based on univariate self-controlled case series (SCCS) design--across six established positive drug-outcome pairs to learn how differences in CDMs and analytics influence steps in the database analytic process and results. Claims data for approximately 7.7 million Humana health plan members were transformed into the two CDMs. Three health outcome cohorts and two drug cohorts showed differences in cohort size and constituency between Mini-Sentinel and OMOP CDMs, which was a result of multiple factors. Overall, the implementation of the HDPS procedure on Mini-Sentinel CDM detected more known positive associations than that on OMOP CDM. The SCCS method results were comparable on both CDMs. Differences in the implementation of the HDPS procedure between the two CDMs were identified; analytic model and risk period specification had a significant impact on the performance of the HDPS procedure on OMOP CDM. Differences were observed between OMOP and Mini-Sentinel CDMs. The analysis of both CDMs at the data model level indicated that such conceptual differences had only a slight but not significant impact on identifying known safety associations. Our results show that differences at the ecosystem level of analyses across the CDMs can lead to strikingly different risk estimations, but this can be primarily attributed to the choices of analytic approach and their implementation in the community-developed analytic tools. The opportunities of using CDMs are clear, but our study shows the need for judicious comparison of analyses across the CDMs. Our work emphasizes the need for ongoing efforts to ensure sustainable transparent platforms to maintain and develop CDMs and associated tools for effective safety surveillance.

Data integration from multiple autonomous data sources has emerged as an important practical problem. The key requirement for such data integration is that owners of such data need to cooperate in a competitive landscape in most of the cases. The research challenge in developing a query processing solution is that the answers to the queries need to be provided while preserving the privacy of the data sources. In general, allowing unrestricted read access to the whole data may give rise to potential vulnerabilities as well as may have legal implications. Therefore, there is a need for privacy preserving database operations for querying data residing at different parties. In this paper, we propose a new query processing technique using third parties in a peer-to-peer system. We propose and evaluate two different protocols for various database operations. Our scheme is able to answer queries without revealing any useful information to the data sources or to the third parties. Analytical comparison of the proposed approach with other recent proposals for privacy-preserving data integration establishes the superiority of the proposed approach in terms of query response time

Many real-world data analyses use common data models (CDMs) to standardize terminologies for medication use, medical events and procedures, data structures, and interpretations of data to facilitate analyses across data sources. For decision makers, key aspects that influence the choice of a CDM may include (i) adaptability to a specific question; (ii) transparency to reproduce findings, assess validity, and instill confidence in findings; and (iii) ease and speed of use. Organizing CDMs preserve the original information from a data source and have maximum adaptability. Full mapping data models, or preconfigured rules systems, are easy to use, since all raw codes are mapped to medical constructs. Adaptive rule systems grow libraries of reusable measures that can easily adjust to preserve adaptability, expedite analyses, and ensure study-specific transparency.

Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration

At present, enterprises have been developing more and more quickly in China. In order to strengthen the systems of enterprise management, manufacturing execution, equipment and so on in each link of management, many enterprises have started to use data integration to construct an Informationization management system. Based on the needs of the development of enterprises, this paper analyzes the pattern formation of data integration and its advantages and explores its role in enterprise informationization construction.

Background and Objectives: Peritoneal dialysis (PD) requires well-functioning medical devices (MDs). PD complications can result in significant adverse events, including the discontinuation of PD, hospitalization, and death. This study aimed to evaluate the feasibility of detecting various PD complications and data related to MDs. Materials and Methods: A retrospective study was conducted on patients who received PD catheter insertions between January 2001 and March 2021 to evaluate PD-related complications. PD complications were evaluated through diagnostic, procedural, and MD codes using a common data model (CDM) and were compared with those from electronic health records (EHRs). The results from one CDM database were compared with those from another CDM database. Results: A total of 342 patients were enrolled. One hundred and ninety-five patients experienced PD complications more than once. Nineteen prescription codes and twenty diagnostic codes from the EHR were identified, covering 11 procedures, three MDs, and seven complications related to PD. Infectious complications were detected using the CDM, whereas mechanical complications were missed. Although data on PD catheters and adaptors were available in the EHR, they were not detected via the CDM. Some infectious and mechanical complications were identified via the CDM in the other database. After implementing amended matching, these data were detected. Conclusions: While some PD-related medical data recorded in EHRs were misrepresented or omitted during the CDM database extraction, transformation, and loading process, the CDM shows potential to serve as a source of real-world data for active surveillance.

Chapter 10 - Data Consolidation and Integration

Empowering the access to public procurement opportunities by means of linking controlled vocabularies. A case study of Product Scheme Classifications in the European e-Procurement sector

Principles of Data Integration

The use of health data supports knowledge-based decision-making in healthcare. Common Data Models (CDMs) and data standards facilitate the integration of diverse data sources and enable federated analysis by harmonizing data formats and terminologies. To determine the best approaches to harmonizing patient data, we undertook a comprehensive literature search, which allowed us to identify the most popular and established CDMs (i2b2, Sentinel CDM, PCORnet CDM, OMOP CDM) and data standards (CDA, HL7 version 2, FHIR, openEHR). We established a set of criteria across the categories of Suitability, Popularity, Adaptability, Interoperability, and Support. The CDMs and data standards are evaluated based on the defined criteria. Overall criteria the OMOP CDM and FHIR scored best. We highlight the strongest CDM and data standard for each criteria category. Given the unique characteristics, strengths, and weaknesses of each CDM and data standard, no single global representation can be selected. To promote broad adoption of CDMs and data standards, it is essential to enable transformation between different representations and utilize various formats within a single tool to facilitate their interoperability. Only then seamless data exchange and research across borders can be achieved. Not applicable.

This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with in-line annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.

Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

Given a very large multimedia database, how to process k-nearest-neighbor queries efficiently? While the sequential scan is one of the most obvious solutions for small-to-moderate multimedia databases, it becomes practically infeasible when the database size grows. Concomitant with the volume and velocity of data, multimedia databases are frequently endowed with a complex distance-based similarity model that supports content-based data access in an adjustable and adaptive manner. Typical for many state-of-the-art distance-based similarity models is an at least quadratic computation time complexity for a single distance evaluation between two multimedia objects. Thus the search for the most query-like multimedia objects is still one of the major challenges.In this paper, we address the problem of optimal k-nearest-neighbor query processing via multiple lower bound approximations in very large multimedia databases. To this end, we propose the concepts of filter-optimality and refinement-optimality and present the Cascading Multi-Step Algorithm and the Interleaved Multi-Step Algorithm for fast query processing. Besides the algorithms’ properties, we study their query processing performance with respect to the number of CPU and I/O operations on large-scale benchmark multimedia databases. Our performance analysis shows how to process k-nearest-neighbor queries in multimedia databases efficiently and provides a guide for further research.

The proposed method includes generation of a “common data model” (CDM) containing all the required parametric information for both CAD and CAE analysis. CDM stores and supplies the associative data to both CAD and CAE models and thus maintaining the associative dependencies between them. As the common data model gets modified according to designer's intent, the changes in it are consistently reflected in both CAD and CAE models through regeneration and analysis iteration. The same data model can be used to work with different CAD and CAE packages since it is totally independent from the software tools used. The data model is reusable and the whole process can be automated so that the embedded expertise in the cycles of the adaptive design and manufacturing can be consistently applied iteratively during product development processes.

Aircraft have multiple disparate data sources associated with maintaining vehicle health. Storing and retrieving data for correlation, trending, and analysis from these data sources is inefficient and expensive. To enable efficient data retrieval and storage, we have implemented an innovative software system based on a common data model that is independent of the data type or vehicle platform. We show that it is possible to integrate data from multiple aircraft platforms using a single data model to facilitate data interoperability and enable cross-platform application development.