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https://www.readbyqxmd.com/read/28329014/prediction-of-chronic-damage-in-systemic-lupus-erythematosus-by-using-machine-learning-models
#1
Fulvia Ceccarelli, Marco Sciandrone, Carlo Perricone, Giulio Galvan, Francesco Morelli, Luis Nunes Vicente, Ilaria Leccese, Laura Massaro, Enrica Cipriano, Francesca Romana Spinelli, Cristiano Alessandri, Guido Valesini, Fabrizio Conti
OBJECTIVE: The increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks. METHODS: We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI)...
2017: PloS One
https://www.readbyqxmd.com/read/28328518/application-of-lms-based-nn-structure-for-power-quality-enhancement-in-a-distribution-network-under-abnormal-conditions
#2
Rahul Kumar Agarwal, Ikhlaq Hussain, Bhim Singh
This paper proposes an application of a least mean-square (LMS)-based neural network (NN) structure for the power quality improvement of a three-phase power distribution network under abnormal conditions. It uses a single-layer neuron structure for the control in a distribution static compensator (DSTATCOM) to attenuate the harmonics such as noise, bias, notches, dc offset, and distortion, injected in the grid current due to connection of several nonlinear loads. This admittance LMS-based NN structure has a simple architecture which reduces the computational complexity and burden which makes it easy to implement...
March 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28328517/convolution-in-convolution-for-network-in-network
#3
Yanwei Pang, Manli Sun, Xiaoheng Jiang, Xuelong Li
Network in network (NiN) is an effective instance and an important extension of deep convolutional neural network consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow multilayer perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and 1 x 1 convolutions in spatial domain, NiN has stronger ability of feature representation and hence results in better recognition performance...
March 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28328516/anrad-a-neuromorphic-anomaly-detection-framework-for-massive-concurrent-data-streams
#4
Qiuwen Chen, Ryan Luley, Qing Wu, Morgan Bishop, Richard W Linderman, Qinru Qiu
The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic...
March 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28328515/solving-multiextremal-problems-by-using-recurrent-neural-networks
#5
Alaeddin Malek, Najmeh Hosseinipour-Mahani
In this paper, a neural network model for solving a class of multiextremal smooth nonconvex constrained optimization problems is proposed. Neural network is designed in such a way that its equilibrium points coincide with the local and global optimal solutions of the corresponding optimization problem. Based on the suitable underestimators for the Lagrangian of the problem, one give geometric criteria for an equilibrium point to be a global minimizer of multiextremal constrained optimization problem with or without bounds on the variables...
March 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28328498/a-framework-for-patient-state-tracking-by-classifying-multiscalar-physiologic-waveform-features
#6
Benjamin Vandendriessche, Mustafa Abas, Thomas Dick, Kenneth Loparo, Frank Jacono
OBJECTIVE: State-of-the-art algorithms that quantify nonlinear dynamics in physiologic waveforms are underutilized clinically due to their esoteric nature. We present a generalizable framework for classifying multiscalar waveform features, designed for patient-state tracking directly at the bedside. METHODS: An artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series...
March 17, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28327711/a-global-coupled-cluster-potential-energy-surface-for-hcl-oh-%C3%A2-cl-h2o
#7
Junxiang Zuo, Bin Zhao, Hua Guo, Daiqian Xie
A new and more accurate full-dimensional global potential energy surface (PES) for the ground electronic state of the ClH2O system is developed by fitting 15 777 points obtained using an explicitly correlated unrestricted coupled-cluster method with single, double, and perturbative triple excitations (UCCSD(T)-F12b). The fitting is carried out using the permutation invariant polynomial-neural network (PIP-NN) method and has an error of 6.9 meV. The new PES has a slightly lower barrier for the atmospherically important HCl + OH → Cl + H2O reaction than the previous PES based on multi-reference configuration interaction (MRCI) calculations...
March 22, 2017: Physical Chemistry Chemical Physics: PCCP
https://www.readbyqxmd.com/read/28327593/characterisation-of-mental-health-conditions-in-social-media-using-informed-deep-learning
#8
George Gkotsis, Anika Oellrich, Sumithra Velupillai, Maria Liakata, Tim J P Hubbard, Richard J B Dobson, Rina Dutta
The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients' own experiences, and only record cross-sectional, professional impressions at the point of care...
March 22, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28327449/integrated-local-binary-pattern-texture-features-for-classification-of-breast-tissue-imaged-by-optical-coherence-microscopy
#9
Sunhua Wan, Hsiang-Chieh Lee, Xiaolei Huang, Ting Xu, Tao Xu, Xianxu Zeng, Zhan Zhang, Yuri Sheikine, James L Connolly, James G Fujimoto, Chao Zhou
This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM...
March 8, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28326656/skin-cancer-diagnosed-by-using-artificial-intelligence-on-clinical-images
#10
Isaäc van der Waal
In a recent Research Letter in Nature an automated classification of a few selected skin lesions has been published, using a deep convolutional neural network (CNN) (Esteva et al, 2017). Convolutional neural network is an important innovation in the field of computer vision. A popular use is for image processing, e.g. applied in face recognition. In the reported study CNN has been applied to a dataset of almost 130,000 clinical images, including some 3,000 dermoscopic images. This article is protected by copyright...
March 22, 2017: Oral Diseases
https://www.readbyqxmd.com/read/28326160/the-neural-correlates-of-childhood-maltreatment-and-the-ability-to-understand-mental-states-of-others
#11
Charlotte C van Schie, Anne-Laura van Harmelen, Kirsten Hauber, Albert Boon, Eveline A Crone, Bernet M Elzinga
Background: Emotional abuse and emotional neglect are related to impaired interpersonal functioning. One underlying mechanism could be a developmental delay in mentalizing, the ability to understand other people's thoughts and emotions. Objective: This study investigates the neural correlates of mentalizing and the specific relationship with emotional abuse and neglect whilst taking into account the level of sexual abuse, physical abuse and physical neglect. Method: The RMET was performed in an fMRI scanner by 46 adolescents (Age: M = 18...
2017: European Journal of Psychotraumatology
https://www.readbyqxmd.com/read/28326014/the-gabaergic-hypothesis-for-cognitive-disabilities-in-down-syndrome
#12
REVIEW
Andrea Contestabile, Salvatore Magara, Laura Cancedda
Down syndrome (DS) is a genetic disorder caused by the presence of a third copy of chromosome 21. DS affects multiple organs, but it invariably results in altered brain development and diverse degrees of intellectual disability. A large body of evidence has shown that synaptic deficits and memory impairment are largely determined by altered GABAergic signaling in trisomic mouse models of DS. These alterations arise during brain development while extending into adulthood, and include genesis of GABAergic neurons, variation of the inhibitory drive and modifications in the control of neural-network excitability...
2017: Frontiers in Cellular Neuroscience
https://www.readbyqxmd.com/read/28326009/improving-eeg-based-driver-fatigue-classification-using-sparse-deep-belief-networks
#13
Rifai Chai, Sai Ho Ling, Phyo Phyo San, Ganesh R Naik, Tuan N Nguyen, Yvonne Tran, Ashley Craig, Hung T Nguyen
This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28325584/development-of-a-surrogate-model-based-on-patient-weight-bone-mass-and-geometry-to-predict-femoral-neck-strains-and-fracture-loads
#14
Mark Taylor, Egon Perilli, Saulo Martelli
Osteoporosis and related bone fractures are an increasing global burden in our ageing society. Areal bone mineral density assessed through dual energy X-ray absorptiometry (DEXA), the clinically accepted and most used method, is not sufficient to assess fracture risk individually. Finite element (FE) modelling has shown improvements in prediction of fracture risk, better than aBMD from DEXA, but is not practical for widespread clinical use. The aim of this study was to develop an adaptive neural network (ANN)-based surrogate model to predict femoral neck strains and fracture loads obtained from a previously developed population-based FE model...
February 27, 2017: Journal of Biomechanics
https://www.readbyqxmd.com/read/28325448/diagnosis-of-autism-through-eeg-processed-by-advanced-computational-algorithms-a-pilot-study
#15
Enzo Grossi, Chiara Olivieri, Massimo Buscema
BACKGROUND: Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28325441/a-review-of-fuzzy-cognitive-maps-in-medicine-taxonomy-methods-and-applications
#16
REVIEW
Abdollah Amirkhani, Elpiniki I Papageorgiou, Akram Mohseni, Mohammad R Mosavi
BACKGROUND AND OBJECTIVE: A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28325033/user-intent-prediction-with-a-scaled-conjugate-gradient-trained-artificial-neural-network-for-lower-limb-amputees-using-a-powered-prosthesis
#17
Richard B Woodward, John A Spanias, Levi J Hargrove
Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28325027/identifying-the-effects-of-visceral-interoception-on-human-brain-connectome-a-multivariate-analysis-of-covariance-of-fmri-data
#18
Behnaz Jarrahi, Dante Mantini
Sources of variations in the neural circuitry of the human brain and interrelationship between intrinsic connectivity networks (ICNs) are still a matter of debate and ongoing research. Here, we applied a multivariate analysis of covariance (MANCOVA) based on high-dimensional independent component analysis (ICA) to identify the effects of interoception and related variables on human brain connectome. Fifteen healthy right-handed subjects (all females, age range 21 - 48 years; mean age = 30.3, SD = 8.7 years) underwent a blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) that included continuous intravesical saline infusion and drainage...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324972/analysis-of-spontaneous-eeg-activity-in-alzheimer-s-disease-using-cross-sample-entropy-and-graph-theory
#19
Carlos Gomez, Jesus Poza, Javier Gomez-Pilar, Alejandro Bachiller, Celia Juan-Cruz, Miguel A Tola-Arribas, Alicia Carreres, Monica Cano, Roberto Hornero
The aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324952/the-influence-of-the-pre-stimulation-neural-state-on-the-post-stimulation-neural-dynamics-via-distributed-microstimulation-of-the-hippocampus
#20
Mark J Connolly, Robert E Gross, Babak Mahmoudi
In this study we investigated how the neural state influences how the brain responds to electrical stimulation using a 16-channel microelectrode array with 8 stimulation and recording channels implanted in the rat hippocampus. In two experiments we identified the stimulation threshold at which the brain changes to an afterdischarge state. In one experiment a range of suprathreshold stimulations were applied, and in another the stimulation was not changed. The neural state was measured by the power spectral density prior to stimulation...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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