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The NHIS complementary medicine datasets remain the primary national source of data on complementary medicine use in the United States.
The obtained DICOM (Digital Imaging and Communications in Medicine) datasets were imported in the computer program AMIRA (version 5.4.2, Visualization Sciences Group, Merignac Cedex, France).
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The data were collected from digital imaging and communications in medicine dataset.
As further applications of this work, we are currently preparing to apply our methodology to a subset of a Traditional Chinese Medicine dataset and a phenotypic screening dataset for Xenopus.
In this paper, a particle swarm optimization-based hierarchical feature selection (PSOHFS) model was proposed to infer potential clinical features of HCC on a Traditional Chinese Medicine dataset which was collected from 120 patients.
Acquired CBCT Digital Imaging and Communications in Medicine (DICOM) datasets were transferred to a computer.
The design patterns we used for the Genomic CDS ontology could potentially be applied to other, similar reasoning problems in the area of genetics and personalized medicine, with datasets that are far larger than in the use-case presented here.
We used a routinely-collected medicine utilisation dataset linked to a longitudinal cohort of adults released from prison to describe the dispensing of contraceptive medication.
Hospital information systems in intensive care medicine generate large datasets on a daily basis.
In England, retesting rates within a year among 15 to 24 year olds in 2010 ranged from 18.4% in the National Chlamydia Screening Program dataset to 26.1% in the genitourinary medicine clinic activity dataset [ 30].
However, there are cases, especially in medicine, where the two datasets, i.e., reference and moving, are acquired with different configurations leading to contrast enhancement (i.e., pre- and post-contrast agent with magnetic resonance imaging).
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