scholarly journals ADT2FHIR – A Tool for Converting ADT/GEKID Oncology Data to HL7 FHIR Resources

2021 ◽  
Author(s):  
Noemi Deppenwiese ◽  
Pierre Delpy ◽  
Mohamed Lambarki ◽  
Martin Lablans
Keyword(s):  
Data Structure ◽  
Clinical Research ◽  
Research Institutions ◽  
Target Structure ◽  
University Hospitals ◽  
Local Data ◽  
Shared Data ◽  
Common Basis ◽  
Hl7 Fhir ◽  
Research Infrastructures

Harmonized and interoperable data management is a core requirement for federated infrastructures in clinical research. Institutions participating in such infrastructures often have to invest large degrees of time and resources in implementing necessary data integration processes to convert their local data to the required target structure. If the data is already available in an alternative shared data structure, the transformation from source to the desired target structure can be implemented once and then be distributed to all participants to reduce effort and harmonize results. The HL7® FHIR® standard is used as a basis for the shared data model of several medical consortia like DKTK and GBA. It is based on so-called resources which can be represented in XML. Oncological data in German university hospitals is commonly available in the ADT/GEKID format. From this common basis we conceptualized and implemented a transformation which accepts ADT/GEKID XML files and returns FHIR resources. We identified several problems with using the general ADT/GEKID structure in federated research infrastructures, as well as some possible pitfalls relating to the FHIR need for resource ids and focus on semantic coding which differs from the approach in the ADT/GEKID standard. To facilitate participation in federated infrastructures, we propose the ADT2FHIR transformation tool for partners with oncological data in the ADT/GEKID format.

scholarly journals Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 270-279
Author(s):  
Quanbao Li ◽  
Fajie Wei ◽  
Shenghan Zhou
Keyword(s):  
Feature Extraction ◽  
Data Structure ◽  
Discriminant Analysis ◽  
Ad Hoc ◽  
Adaptive Method ◽  
Local Data ◽  
Linear Discriminant ◽  
Linear Feature ◽  
Nonparametric Discriminant Analysis ◽  
Nonlinear Feature Extraction

AbstractThe linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

Piloting the EHR4CR Feasibility Platform across Europe

2014 ◽  
Vol 53 (04) ◽  
pp. 264-268 ◽  
Author(s):  
R. Bache ◽  
M. McGilchrist ◽  
C. Daniel ◽  
M. Dugas ◽  
F. Fritz ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Test Data ◽  
Data Access ◽  
Data Warehouses ◽  
University Hospitals ◽  
Local Networks ◽  
Health Records ◽  
Electronic Health ◽  
Set Up

SummaryBackground: Pharmaceutical clinical trials are primarily conducted across many countries, yet recruitment numbers are frequently not met in time. Electronic health records store large amounts of potentially useful data that could aid in this process. The EHR4CR project aims at re-using EHR data for clinical research purposes.Objective: To evaluate whether the protocol feasibility platform produced by the Electronic Health Records for Clinical Research (EHR4CR) project can be installed and set up in accordance with local technical and governance requirements to execute protocol feasibility queries uniformly across national borders.Methods: We installed specifically engineered software and warehouses at local sites. Approvals for data access and usage of the platform were acquired and terminology mapping of local site codes to central platform codes were performed. A test data set, or real EHR data where approvals were in place, were loaded into data warehouses. Test feasibility queries were created on a central component of the platform and sent to the local components at eleven university hospitals.Results: To use real, de-identified EHR data we obtained permissions and approvals from ‘data controllers‘ and ethics committees. Through the platform we were able to create feasibility queries, distribute them to eleven university hospitals and retrieve aggregated patient counts of both test data and de-identified EHR data.Conclusion: It is possible to install a uniform piece of software in different university hospitals in five European countries and configure it to the requirements of the local networks, while complying with local data protection regulations. We were also able set up ETL processes and data warehouses, to reuse EHR data for feasibility queries distributed over the EHR4CR platform.

scholarly journals The US–Latin America Cancer Research Network

2020 ◽  
Vol 6 (Supplement_1) ◽  
pp. 56-56
Author(s):  
Vidya Vedham ◽  
Marianne K. Henderson ◽  
Osvaldo Podhajcer ◽  
Andrea Llera ◽  
Marisa Dreyer Breitenbach ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Research ◽  
Latin America ◽  
Clinical Research ◽  
Latin American ◽  
Molecular Profiling ◽  
Research Network ◽  
Research Institutions ◽  
Cancer Research Network ◽  
The Us

PURPOSE The National Cancer Institute (NCI) Center for Global Health promotes global oncology research to reduce cancer burden worldwide. In 2009, NCI launched the Latin American Cancer Research Network (LACRN) to support a clinical cancer research network in Latin America. LACRN was started by a coalition of research institutions through bilateral collaborative agreements between the US Department of Health and Human Services and the governments of Argentina, Brazil, Chile, Mexico, and Uruguay. The LACRN is supported through a research contract to a study coordination center and subcontracts to 6 low- and middle-income country sites. The participating countries have a shared goal that meets the specific research needs of the regions. The overarching purpose of this endeavor is to implement high-quality standards for conducting clinical research studies and developing collaborative cancer research projects. METHODS NCI supported a clinical breast cancer project for LACRN, “Molecular profiling of breast cancer (MPBC) in Latin American women with stage II and III breast cancer receiving standard neo-adjuvant chemotherapy.” The molecular profiling of breast cancer study was conducted in 40 hospitals and research institutions across 5 countries with a study population of approximately 1,400 patients. RESULTS AND CONCLUSION Establishing a comprehensive network in Latin America and their research institutions yielded an incredible research resource that can be used in future studies, driven by the network. Throughout the process of developing and implementing studies, LACRN helped identify key elements of the functionality of research networks, such as the pivotal role of institutional and government commitment for sustainability; the importance of building multidisciplinary teams, transparent communications, and training; the ability to combine translational, epidemiology, and clinical research to close research gaps; and the application of new technologies to standard cancer clinical care.

Development of an Open Metadata Schema for Prospective Clinical Research (openPCR) in China

2014 ◽  
Vol 53 (01) ◽  
pp. 39-46 ◽  
Author(s):  
Z. Guan ◽  
J. Sun ◽  
Z. Wang ◽  
Y. Geng ◽  
W. Xu
Keyword(s):  
Data Structure ◽  
Clinical Research ◽  
Clinical Data ◽  
Early Stage ◽  
Data Entry ◽  
Data Management System ◽  
Case Report Form ◽  
Functional Requirements ◽  
The Core ◽  
Metadata Schema

SummaryObjectives: In China, deployment of electronic data capture (EDC) and clinical data management system (CDMS) for clinical research (CR) is in its very early stage, and about 90% of clinical studies collected and submitted clinical data manually. This work aims to build an open metadata schema for Prospective Clinical Research (openPCR) in China based on openEHR archetypes, in order to help Chinese researchers easily create specific data entry templates for registration, study design and clinical data collection.Methods: Singapore Framework for Dublin Core Application Profiles (DCAP) is used to develop openPCR and four steps such as defining the core functional requirements and deducing the core metadata items, developing archetype models, defining metadata terms and creating archetype records, and finally developing implementation syntax are followed.Results: The core functional requirements are divided into three categories: requirements for research registration, requirements for trial design, and requirements for case report form (CRF). 74 metadata items are identified and their Chinese authority names are created. The minimum metadata set of openPCR includes 3 documents, 6 sections, 26 top level data groups, 32 lower data groups and 74 data elements. The top level container in openPCR is composed of public document, internal document and clinical document archetypes. A hierarchical structure of openPCR is established according to Data Structure of Electronic Health Record Architecture and Data Stand -ard of China (Chinese EHR Standard). Meta-data attributes are grouped into six parts: identification, definition, representation, relation, usage guides, and administration.Discussions and Conclusion: OpenPCR is an open metadata schema based on research registration standards, standards of the Clinical Data Interchange Standards Consortium (CDISC) and Chinese healthcare related stand -ards, and is to be publicly available throughout China. It considers future integration of EHR and CR by adopting data structure and data terms in Chinese EHR Standard. Archetypes in openPCR are modularity models and can be separated, recombined, and reused. The authors recommend that the method to develop openPCR can be referenced by other countries when designing metadata schema of clinical research. In the next steps, openPCR should be used in a number of CR projects to test its applicability and to continuously improve its coverage. Besides, metadata schema for research protocol can be developed to structurize and standardize protocol, and syntactical interoperability of openPCR with other related standards can be considered.

scholarly journals Ordinal Distance Metric Learning with MDS for Image Ranking

2018 ◽  
Vol 35 (01) ◽  
pp. 1850007 ◽  
Author(s):  
Panpan Yu ◽  
Qingna Li
Keyword(s):  
Data Structure ◽  
Metric Learning ◽  
Learning Model ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Local Data ◽  
Linear Distance ◽  
Image Ranking ◽  
Unconstrained Problem ◽  
The Cost

Image ranking is to rank images based on some known ranked images. In this paper, we propose an improved linear ordinal distance metric learning approach based on the linear distance metric learning model. By decomposing the distance metric [Formula: see text] as [Formula: see text], the problem can be cast as looking for a linear map between two sets of points in different spaces, meanwhile maintaining some data structures. The ordinal relation of the labels can be maintained via classical multidimensional scaling, a popular tool for dimension reduction in statistics. A least squares fitting term is then introduced to the cost function, which can also maintain the local data structure. The resulting model is an unconstrained problem, and can better fit the data structure. Extensive numerical results demonstrate the improvement of the new approach over the linear distance metric learning model both in speed and ranking performance.

scholarly journals Compassionate use of interventions: results of a European Clinical Research Infrastructures Network (ECRIN) survey of ten European countries

Trials ◽  
2010 ◽  
Vol 11 (1) ◽  
Author(s):  
Kate Whitfield ◽  
Karl-Heinz Huemer ◽  
Diana Winter ◽  
Steffen Thirstrup ◽  
Christian Libersa ◽  
...  
Keyword(s):  
Clinical Research ◽  
European Countries ◽  
Compassionate Use ◽  
Research Infrastructures
Sign in / Sign up

Export Citation Format

Share Document