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https://www.readbyqxmd.com/read/28231416/feasibility-of-combining-common-data-elements-across-studies-to-test-a-hypothesis
#1
Elizabeth J Corwin, Shirley M Moore, Andrea Plotsky, Margaret M Heitkemper, Susan G Dorsey, Drenna Waldrop-Valverde, Donald E Bailey, Sharron L Docherty, Joanne D Whitney, Carol M Musil, Cynthia M Dougherty, Donna J McCloskey, Joan K Austin, Patricia A Grady
PURPOSE: The purpose of this article is to describe the outcomes of a collaborative initiative to share data across five schools of nursing in order to evaluate the feasibility of collecting common data elements (CDEs) and developing a common data repository to test hypotheses of interest to nursing scientists. This initiative extended work already completed by the National Institute of Nursing Research CDE Working Group that successfully identified CDEs related to symptoms and self-management, with the goal of supporting more complex, reproducible, and patient-focused research...
February 23, 2017: Journal of Nursing Scholarship
https://www.readbyqxmd.com/read/28227968/limb-position-robust-classification-of-myoelectric-signals-for-prosthesis-control-using-sparse-representations
#2
Joseph L Betthauser, Christopher L Hunt, Luke E Osborn, Rahul R Kaliki, Nitish V Thakor, Joseph L Betthauser, Christopher L Hunt, Luke E Osborn, Rahul R Kaliki, Nitish V Thakor, Luke E Osborn, Christopher L Hunt, Nitish V Thakor, Rahul R Kaliki, Joseph L Betthauser
The fundamental objective in non-invasive myoelectric prosthesis control is to determine the user's intended movements from corresponding skin-surface recorded electromyographic (sEMG) activation signals as quickly and accurately as possible. Linear Discriminant Analysis (LDA) has emerged as the de facto standard for real-time movement classification due to its ease of use, calculation speed, and remarkable classification accuracy under controlled training conditions. However, performance of cluster-based methods like LDA for sEMG pattern recognition degrades significantly when real-world testing conditions do not resemble the trained conditions, limiting the utility of myoelectrically controlled prosthesis devices...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227266/dictionary-learning-for-sparse-representation-and-classification-of-neural-spikes
#3
Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Douglas Weber, Zhi-Hong Mao, Yiran Chen, Ahmed H Dallal
Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering. In our method, we use Fisher discriminant based dictionary learning to learn dictionary, whose sub-dictionaries are class specific, and estimate discriminative sparse coding coefficients by minimizing the within class scatter and maximizing the between class scatter...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227018/using-constrained-information-entropy-to-detect-rare-adverse-drug-reactions-from-medical-forums
#4
Yi Zheng, Chaowang Lan, Hui Peng, Jinyan Li, Yi Zheng, Chaowang Lan, Hui Peng, Jinyan Li, Jinyan Li, Hui Peng, Chaowang Lan, Yi Zheng
Adverse drug reactions (ADRs) detection is critical to avoid malpractices yet challenging due to its uncertainty in pre-marketing review and the underreporting in post-marketing surveillance. To conquer this predicament, social media based ADRs detection methods have been proposed recently. However, existing researches are mostly co-occurrence based methods and face several issues, in particularly, leaving out the rare ADRs and unable to distinguish irrelevant ADRs. In this work, we introduce a constrained information entropy (CIE) method to solve these problems...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226751/deep-neural-ensemble-for-retinal-vessel-segmentation-in-fundus-images-towards-achieving-label-free-angiography
#5
A Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas, A Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas, Debdoot Sheet, Prabir Kumar Biswas, A Lahiri
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#6
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Xinyang Feng, Jie Yang, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226530/low-rank-magnetic-resonance-fingerprinting
#7
Gal Mazor, Lior Weizman, Assaf Tal, Yonina C Eldar, Gal Mazor, Lior Weizman, Assaf Tal, Yonina C Eldar, Gal Mazor, Assaf Tal, Lior Weizman, Yonina C Eldar
Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226521/eda-gram-designing-electrodermal-activity-fingerprints-for-visualization-and-feature-extraction
#8
Theodora Chaspari, Andreas Tsiartas, Leah I Stein Duker, Sharon A Cermak, Shrikanth S Narayanan, Theodora Chaspari, Andreas Tsiartas, Leah I Stein Duker, Sharon A Cermak, Shrikanth S Narayanan, Andreas Tsiartas, Theodora Chaspari, Sharon A Cermak, Shrikanth S Narayanan, Leah I Stein Duker
Wearable technology permeates every aspect of our daily life increasing the need of reliable and interpretable models for processing the large amount of biomedical data. We propose the EDA-Gram, a multidimensional fingerprint of the electrodermal activity (EDA) signal, inspired by the widely-used notion of spectrogram. The EDA-Gram is based on the sparse decomposition of EDA from a knowledge-driven set of dictionary atoms. The time axis reflects the analysis frames, the spectral dimension depicts the width of selected dictionary atoms, while intensity values are computed from the atom coefficients...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28225061/dictionary-enhanced-imaging-cytometry
#9
Antony Orth, Diane Schaak, Ethan Schonbrun
State-of-the-art high-throughput microscopes are now capable of recording image data at a phenomenal rate, imaging entire microscope slides in minutes. In this paper we investigate how a large image set can be used to perform automated cell classification and denoising. To this end, we acquire an image library consisting of over one quarter-million white blood cell (WBC) nuclei together with CD15/CD16 protein expression for each cell. We show that the WBC nucleus images alone can be used to replicate CD expression-based gating, even in the presence of significant imaging noise...
February 22, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28223063/matrix-completion-based-reconstruction-for-undersampled-magnetic-resonance-fingerprinting-data
#10
Mariya Doneva, Thomas Amthor, Peter Koken, Karsten Sommer, Peter Börnert
An iterative reconstruction method for undersampled magnetic resonance fingerprinting data is presented. The method performs the reconstruction entirely in k-space and is related to low rank matrix completion methods. A low dimensional data subspace is estimated from a small number of k-space locations fully sampled in the temporal direction and used to reconstruct the missing k-space samples before MRF dictionary matching. Performing the iterations in k-space eliminates the need for applying a forward and an inverse Fourier transform in each iteration required in previously proposed iterative reconstruction methods for undersampled MRF data...
February 18, 2017: Magnetic Resonance Imaging
https://www.readbyqxmd.com/read/28222790/the-bear-in-eurasian-plant-names-motivations-and-models
#11
REVIEW
Valeria Kolosova, Ingvar Svanberg, Raivo Kalle, Lisa Strecker, Ayşe Mine Gençler Özkan, Andrea Pieroni, Kevin Cianfaglione, Zsolt Molnár, Nora Papp, Łukasz Łuczaj, Dessislava Dimitrova, Daiva Šeškauskaitė, Jonathan Roper, Avni Hajdari, Renata Sõukand
Ethnolinguistic studies are important for understanding an ethnic group's ideas on the world, expressed in its language. Comparing corresponding aspects of such knowledge might help clarify problems of origin for certain concepts and words, e.g. whether they form common heritage, have an independent origin, are borrowings, or calques. The current study was conducted on the material in Slavonic, Baltic, Germanic, Romance, Finno-Ugrian, Turkic and Albanian languages. The bear was chosen as being a large, dangerous animal, important in traditional culture, whose name is widely reflected in folk plant names...
February 21, 2017: Journal of Ethnobiology and Ethnomedicine
https://www.readbyqxmd.com/read/28214787/sparse-and-dense-hybrid-representation-via-subspace-modeling-for-dynamic-mri
#12
Qiegen Liu, Shanshan Wang, Dong Liang
Recent theoretical results on compressed sensing and low-rank matrix recovery have inspired significant interest in joint sparse and low rank modeling of dynamic magnetic resonance imaging (dMRI). Existing approaches usually describe these two respective prior information with different formulations. In this paper, we present a novel sparse and dense hybrid representation (SDR) model which describes the sparse plus low rank properties by a unified way. More specifically, under the learned dictionary consisting of temporal basis functions, SDR models the spatial coefficients in two subspaces with Laplacian and Gaussian prior distributions, respectively...
February 5, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28212114/discriminative-feature-representation-an-effective-postprocessing-solution-to-low-dose-ct-imaging
#13
Yang Chen, Jin Liu, Yining Hu, Jian Yang, Luyao Shi, Huazhong Shu, Zhiguo Gui, Gouenou Coatrieux, Limin Luo
This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality...
February 17, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28212079/multi-view-multi-instance-learning-based-on-joint-sparse-representation-and-multi-view-dictionary-learning
#14
Bing Li, Chunfeng Yuan, Weihua Xiong, Weiming Hu, Houwen Peng, Xinmiao Ding, Stephen Maybank
In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (M2IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse "-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M2IL...
February 14, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28211644/healthcare-professionals-and-pharmacovigilance-of-pediatric-adverse-drug-reactions-a-5-year-analysis-of-adverse-events-reporting-system-database-of-the-food-and-drug-administration
#15
Caterina Bigi, Marco Tuccori, Guido Bocci
BACKGROUND: To analyze the Adverse Events Reporting System (AERS) database of the Food and Drug Administration (FDA), investigating the characteristics of pediatric adverse drug reactions (ADRs) and describing the effective participation of healthcare professionals in the reporting activity. METHODS: Reports of ADRs were obtained from the FDA website. Only ADRs in pediatric subjects (divided by age, by country and by professional category) were included into the analysis...
February 17, 2017: Minerva Pediatrica
https://www.readbyqxmd.com/read/28205084/-the-republic-of-humours-scholarly-quarrels-in-bayle-s-dictionary
#16
Isabelle Moreau
Quarrelling is a 'routine' activity of the Republic of Letters. This article demonstrates that quarrels played a key role in the field of historical criticism. The contention of this article is twofold. First, it explores the epistemological issues raised by Bayle while reporting the quarrels of the Republic of Letters, and demonstrates their creative potential, thus applying to historiography conclusions drawn by recent research on scientific controversies. It offers a new understanding of scholarly quarrels, here understood as a socially and intellectually structuring activity...
December 2016: Revue de Synthèse
https://www.readbyqxmd.com/read/28194221/a-removal-of-eye-movement-and-blink-artifacts-from-eeg-data-using-morphological-component-analysis
#17
Balbir Singh, Hiroaki Wagatsuma
EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of "dictionary...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28187898/vessel-segmentation-and-microaneurysm-detection-using-discriminative-dictionary-learning-and-sparse-representation
#18
Malihe Javidi, Hamid-Reza Pourreza, Ahad Harati
Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28182553/multi-dimensional-sparse-models
#19
Na Qi, Yunhui Shi, Xiaoyan Sun, Jingdong Wang, Baocai Yin, Junbin Gao
Traditional synthesis/analysis sparse representation models signals in a one dimensional (1D) way, in which a multidimensional (MD) signal is converted into a 1D vector. 1D modeling cannot sufficiently handle MD signals of high dimensionality in limited computational resources and memory usage, as breaking the data structure and inherently ignores the diversity of MD signals (tensors). We utilize the multilinearity of tensors to establish the redundant basis of the space of multi linear maps with the sparsity constraint, and further propose MD synthesis/analysis sparse models to effectively and efficiently represent MD signals in their original form...
February 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28177548/7t-guided-super-resolution-of-3t-mri
#20
Khosro Bahrami, Feng Shi, Islem Rekik, Yaozong Gao, Dinggang Shen
PURPOSE: High-resolution MR images can depict rich details of brain anatomical structures and show subtle changes in longitudinal data. 7T MRI scanners can acquire MR images with higher resolution and better tissue contrast than the routine 3T MRI scanners. However, 7T MRI scanners are currently more expensive and less available in clinical and research centers. To this end, we propose a method to generate super-resolution 3T MRI that resembles 7T MRI, which is called as 7T-like MR image in this paper...
February 8, 2017: Medical Physics
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