Korean National Institute of Health initiated data harmonization across cohorts with the aim to ensure semantic interoperability of data and to create a common database of standardized data elements for future collaborative research. With this aim, we reviewed code books of cohorts and identified common data items and values which can be combined for data analyses. We then mapped data items and values to standard health terminologies such as SNOMED CT. Preliminary results of this ongoing data harmonization work will be presented.
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http://dx.doi.org/10.3233/SHTI240813 | DOI Listing |
In much of the northern Great Basin of the western United States, rangelands, and semi-arid ecosystems invaded by exotic annual grasses such as cheatgrass () and medusahead () are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground-based field methods for measuring the above-ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes and better harmonize with other remote sensing techniques. Developments and increased adoption of unoccupied aerial systems (UAS) and instrumentation for vegetation monitoring enable greater understanding of vegetation in many ecosystems.
View Article and Find Full Text PDFOpen Forum Infect Dis
January 2025
Northwell, Department of Pathology and Laboratory Medicine, New Hyde Park, New York, USA.
Background: We developed a United States-based real-world data resource to better understand the continued impact of the coronavirus disease 2019 (COVID-19) pandemic on immunocompromised patients, who are typically underrepresented in prospective studies and clinical trials.
Methods: The COVID-19 Real World Data infrastructure (CRWDi) was created by linking and harmonizing de-identified HealthVerity medical and pharmacy claims data from 1 December 2018 to 31 December 2023, with severe acute respiratory syndrome coronavirus 2 virologic and serologic laboratory data from major commercial laboratories and Northwell Health; COVID-19 vaccination data; and, for patients with cancer, 2010 to 2021 National Cancer Institute Surveillance, Epidemiology, and End Results registry data.
Results: The CRWDi contains 4 cohorts: patients with cancer; patients with rheumatic diseases receiving pharmacotherapy; noncancer solid organ and hematopoietic stem cell transplant recipients; and people from the general population including adults and pediatric patients.
J Nucl Med
January 2025
United Theranostics, Bethesda, Maryland.
Computational nuclear oncology for precision radiopharmaceutical therapy (RPT) is a new frontier for theranostic treatment personalization. A key strategy relies on the possibility to incorporate clinical, biomarker, image-based, and dosimetric information in theranostic digital twins (TDTs) of patients to move beyond a one-size-fits-all approach. The TDT framework enables treatment optimization by real-time monitoring of the real-world system, simulation of different treatment scenarios, and prediction of resulting treatment outcomes, as well as facilitating collaboration and knowledge sharing among health care professionals adopting a harmonized TDT.
View Article and Find Full Text PDFDigit Health
January 2025
OPEN Health, London, UK.
Objective: Digital therapeutics (DTx) are promising technologies. However, current assessment and access frameworks, when they exist, are heterogeneous and fragmented. We analysed and compared health technology assessment (HTA) criteria for DTx across European countries that had assessed the same DTx products.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States.
Background: Cohort studies contain rich clinical data across large and diverse patient populations and are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alternative is to harmonize data from existing cohorts through multicohort studies. However, given differences in variable encoding, accurate variable harmonization is difficult.
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