A New Classification Algorithm and a New Oversampling Method of Mapping Common Data Elements to the BRIDG Model

Introduction: Multiple data collections can be a burden for clinicians. In 2009, the Australian Stroke Clinical Registry (AuSCR) was established by non-government and research organizations to provide quality of care data unavailable for acute stroke admissions. We show here the reliability of linking complimentary registry data with routinely collected hospital discharge data submitted to governmental bodies. Hypothesis: A high quality linkage with a > 90% rate is possible, but requires multiple personal identifiers common to each dataset. Methods: AuSCR identifying variables included date of birth (DoB), Medicare number, first name, surname, postcode, gender, hospital record number, hospital name and admission date. The Victorian Department of Health emergency department (ED) and hospital discharge linked dataset has most of these, with first name truncated to the first 3 digits, but no surname. Common data elements of AuSCR patients registered at a large hospital in Melbourne, Victoria (Australia) between 15 June 2009 and 31 December 2010 were submitted to undergo stepwise deterministic linkage. Results: The Victorian AuSCR sample had 818 records from 788 individuals. Three steps with 1) Medicare number, postcode, gender and DoB (80% matched); 2) hospital number/admit date; and 3) ED number/visit date were required to link AuSCR data with the ED and hospital discharge data. These led to an overall high quality linkage of >99% (782/788) of AuSCR patients, including 731/788 for ED records and 736/788 for hospital records. Conclusion: Multiple personal identifiers from registries are required to achieve reliable linkage to routinely collected hospital data. Benefits of these linked data include the ability to investigate a broader range of research questions than with a single dataset. Characters with spaces= 1941 (limit is 1950)

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Preclinical common data elements (CDEs) for epilepsy: A joint ILAE/AES and NINDS translational initiative

Epilepsia Open ◽

10.1002/epi4.12235 ◽

2018 ◽

Vol 3 ◽

pp. 9-12 ◽

Cited By ~ 3

Author(s):

Helen E. Scharfman ◽

Aristea S. Galanopoulou ◽

Jacqueline A. French ◽

Asla Pitkänen ◽

Vicky Whittemore ◽

...

Keyword(s):

Common Data Elements ◽

Data Elements

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Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies

BioMed Research International ◽

10.1155/2015/976272 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

A. Anil Sinaci ◽

Gokce B. Laleci Erturkmen ◽

Suat Gonul ◽

Mustafa Yuksel ◽

Paolo Invernizzi ◽

...

Keyword(s):

Clinical Research ◽

Real Life ◽

Critical Factor ◽

Drug Surveillance ◽

Safety Study ◽

Data Interoperability ◽

Common Data Elements ◽

Lombardy Region ◽

Research Activities ◽

Data Elements

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.

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Domain-specific Common Data Elements for Rare Disease Registration: A Conceptual Approach of a European Joint Initiative towards Semantic Interoperability in Rare Disease Research (Preprint)

JMIR Medical Informatics ◽

10.2196/32158 ◽

2021 ◽

Author(s):

Haitham Abaza ◽

Dennis Kadioglu ◽

Simona Martin ◽

Andri Papadopoulou ◽

Bruna dos Santos Viera ◽

...

Keyword(s):

Rare Disease ◽

Semantic Interoperability ◽

Conceptual Approach ◽

Common Data Elements ◽

Domain Specific ◽

Disease Research ◽

Data Elements

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A New Classification Algorithm integrating Multi-temporal Data.

Journal of the Japan society of photogrammetry and remote sensing ◽

10.4287/jsprs.31.4_45 ◽

1992 ◽

Vol 31 (4) ◽

pp. 45-54

Author(s):

Sunpyo HONG ◽

Kiyonari FUKUE ◽

Haruhisa SHIMODA ◽

Toshibumi SAKATA

Keyword(s):

Classification Algorithm ◽

Temporal Data ◽

New Classification ◽

Multi Temporal

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The Military Suicide Research Consortium Common Data Elements: An Examination of Measurement Invariance Across Current Service Members and Veterans

Assessment ◽

10.1177/1073191118777635 ◽

2018 ◽

Vol 26 (6) ◽

pp. 963-975 ◽

Cited By ~ 1

Author(s):

Ian H. Stanley ◽

Jennifer M. Buchman-Schmitt ◽

Carol Chu ◽

Megan L. Rogers ◽

Anna R. Gai ◽

...

Keyword(s):

Measurement Invariance ◽

Model Fit ◽

Service Members ◽

Common Data Elements ◽

Adequate Model ◽

Increased Risk ◽

Research Consortium ◽

Data Elements ◽

Military Suicide ◽

The Military

Suicide rates within the U.S. military are elevated, necessitating greater efforts to identify those at increased risk. This study utilized a multigroup confirmatory factor analysis to examine measurement invariance of the Military Suicide Research Consortium Common Data Elements (CDEs) across current service members ( n = 2,015), younger veterans (<35 years; n = 377), and older veterans (≥35 years; n = 1,001). Strong factorial invariance was supported with adequate model fit observed for current service members, younger veterans, and older veterans. The structures of all models were generally comparable with few exceptions. The Military Suicide Research Consortium CDEs demonstrate at least adequate model fit for current military service members and veterans, regardless of age. Thus, the CDEs can be validly used across military and veteran populations. Given similar latent structures, research findings in one group may inform clinical and policy decision making for the other.

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Reexamining COVID-19 Self-Reported Symptom Tracking Programs in the United States: An Updated Framework Synthesis (Preprint)

10.2196/preprints.31271 ◽

2021 ◽

Author(s):

Miranda Lynn Janvrin ◽

Jessica Korona-Bailey ◽

Tracey Pérez Koehlmoos

Keyword(s):

The United States ◽

Time Frame ◽

Vaccine Hesitancy ◽

Demographic Information ◽

Common Data Elements ◽

Intended Audience ◽

The Public ◽

Pandemic Response ◽

Data Elements ◽

Google Search

BACKGROUND Early in the pandemic Koehlmoos et al (2020) completed a framework synthesis of currently available self-reported symptom tracking programs for COVID19. This framework described the programs, partners/affiliates, funding, responses, platform, and intended audience, among other considerations. OBJECTIVE This current study seeks to update the existing framework with the aim of identifying developments in the landscape and highlighting how programs have adapted to changes in pandemic response. METHODS Our team developed a framework to collate information on current COVD19 self-reported symptom tracking programs using the best-framework method. All programs from the previous article were included to document changes. New programs were discovered using a Google search for keywords. The time frame for the search for programs ranges from March 1, 2021, to May 6, 2021. RESULTS We screened 33 programs; 8 were included in our final framework synthesis. We identified multiple common data elements, including demographic information like race, age, gender, and affiliation (all were associated with universities, medical schools, or schools of public health). Dissimilarities included questions regarding vaccination status, vaccine hesitancy, social distancing adherence, testing, and mental health. CONCLUSIONS At this time, the future of self-reported symptom tracking for COVID-19 is unclear. Some sources have speculated that COVID-19 may become a yearly occurrence much like the flu, and if so, the data that these programs generate is still valuable. However, it is unclear if the public will maintain the same level of interest in reporting their symptoms on a regular basis if the COVID19 becomes more routine.

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