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https://www.readbyqxmd.com/read/29353183/automatic-bad-channel-detection-in-intracranial-electroencephalographic-recordings-using-ensemble-machine-learning
#1
Viateur Tuyisenge, Lena Trebaul, Manik Bhattacharjee, Blandine Chanteloup-Forêt, Carole Saubat-Guigui, Ioana Mîndruţă, Sylvain Rheims, Louis Maillard, Philippe Kahane, Delphine Taussig, Olivier David
OBJECTIVE: Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. METHODS: The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions...
December 24, 2017: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
https://www.readbyqxmd.com/read/29353161/automated-diagnosis-of-focal-liver-lesions-using-bidirectional-empirical-mode-decomposition-features
#2
U Rajendra Acharya, Joel En Wei Koh, Yuki Hagiwara, Jen Hong Tan, Arkadiusz Gertych, Anushya Vijayananthan, Nur Adura Yaakup, Basri Johan Jeet Abdullah, Mohd Kamil Bin Mohd Fabell, Chai Hong Yeong
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions...
January 3, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29353160/prediction-of-venous-thromboembolism-using-semantic-and-sentiment-analyses-of-clinical-narratives
#3
Susan Sabra, Khalid Mahmood Malik, Mazen Alobaidi
Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The increased number of VTE cases with a high fatality rate of 25% at first occurrence makes preventive measures essential. Clinical narratives are a rich source of knowledge and should be included in the diagnosis and treatment processes, as they may contain critical information on risk factors. It is very important to make such narrative blocks of information usable for searching, health analytics, and decision-making...
January 3, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29352978/use-of-multimodality-imaging-and-artificial-intelligence-for-diagnosis-and-prognosis-of-early-stages-of-alzheimer-s-disease
#4
REVIEW
Xiaonan Liu, Kewei Chen, Teresa Wu, David Weidman, Fleming Lure, Jing Li
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information...
January 10, 2018: Translational Research: the Journal of Laboratory and Clinical Medicine
https://www.readbyqxmd.com/read/29352548/prediction-of-psychosis-across-protocols-and-risk-cohorts-using-automated-language-analysis
#5
Cheryl M Corcoran, Facundo Carrillo, Diego Fernández-Slezak, Gillinder Bedi, Casimir Klim, Daniel C Javitt, Carrie E Bearden, Guillermo A Cecchi
Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e...
February 2018: World Psychiatry: Official Journal of the World Psychiatric Association (WPA)
https://www.readbyqxmd.com/read/29352405/virus-particle-detection-by-convolutional-neural-network-in-transmission-electron-microscopy-images
#6
Eisuke Ito, Takaaki Sato, Daisuke Sano, Etsuko Utagawa, Tsuyoshi Kato
A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps...
January 19, 2018: Food and Environmental Virology
https://www.readbyqxmd.com/read/29352380/integrated-prediction-of-lesion-specific-ischaemia-from-quantitative-coronary-ct-angiography-using-machine-learning-a-multicentre-study
#7
Damini Dey, Sara Gaur, Kristian A Ovrehus, Piotr J Slomka, Julian Betancur, Markus Goeller, Michaela M Hell, Heidi Gransar, Daniel S Berman, Stephan Achenbach, Hans Erik Botker, Jesper Moller Jensen, Jens Flensted Lassen, Bjarne Linde Norgaard
OBJECTIVES: We aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomography angiography (CTA). METHODS: In a multicentre trial of 254 patients, CTA and invasive coronary angiography were performed, with FFR in 484 vessels. CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length...
January 19, 2018: European Radiology
https://www.readbyqxmd.com/read/29352185/genetic-fingerprinting-of-salmon-louse-lepeophtheirus-salmonis-populations-in-the-north-east-atlantic-using-a-random-forest-classification-approach
#8
A Jacobs, M De Noia, K Praebel, Ø Kanstad-Hanssen, M Paterno, D Jackson, P McGinnity, A Sturm, K R Elmer, M S Llewellyn
Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic Lepeophtheirus salmonis, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations...
January 19, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29352006/machine-learning-in-cardiovascular-medicine-are-we-there-yet
#9
REVIEW
Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform...
January 19, 2018: Heart: Official Journal of the British Cardiac Society
https://www.readbyqxmd.com/read/29351762/inclusion-of-edaphic-predictors-for-enhancement-of-models-to-determine-distribution-of-soil-transmitted-helminths-the-case-of-zimbabwe
#10
Nicholas Midzi, Blessing Kavhu, Portia Manangazira, Isaac Phiri, Susan L Mutambu, Cremants Tshuma, Moses J Chimbari, Shungu Munyati, Stanely M Midzi, Lincon Charimari, Anatoria Ncube, Masceline J Mutsaka-Makuvaza, White Soko, Emmanuel Madzima, Gibson Hlerema, Joel Mbedzi, Gibson Mhlanga, Mhosisi Masocha
BACKGROUND: Reliable mapping of soil-transmitted helminth (STH) parasites requires rigorous statistical and machine learning algorithms capable of integrating the combined influence of several determinants to predict distributions. This study tested whether combining edaphic predictors with relevant environmental predictors improves model performance when predicting the distribution of STH, Ascaris lumbricoides and hookworms at a national scale in Zimbabwe. METHODS: Geo-referenced parasitological data obtained from a 2010/2011 national survey indicating a confirmed presence or absence of STH among school children aged 10-15 years was used to calibrate ten species distribution models (SDMs)...
January 19, 2018: Parasites & Vectors
https://www.readbyqxmd.com/read/29351669/how-pairwise-coevolutionary-models-capture-the-collective-residue-variability-in-proteins
#11
Matteo Figliuzzi, Pierre Barrat-Charlaix, Martin Weigt
Global coevolutionary models of homologous protein families, as constructed by direct coupling analysis (DCA), have recently gained popularity in particular due to their capacity to accurately predict residueresidue contacts from sequence information alone, and thereby to facilitate tertiary and quaternary protein structure prediction. More recently, they have also been used to predict fitness effects of aminoacid substitutions in proteins, and to predict evolutionary conserved protein-protein interactions...
January 17, 2018: Molecular Biology and Evolution
https://www.readbyqxmd.com/read/29351266/from-extraction-of-local-structures-of-protein-energy-landscapes-to-improved-decoy-selection-in-template-free-protein-structure-prediction
#12
Nasrin Akhter, Amarda Shehu
Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging...
January 19, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/29351262/imu-to-segment-assignment-and-orientation-alignment-for-the-lower-body-using-deep-learning
#13
Tobias Zimmermann, Bertram Taetz, Gabriele Bleser
Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles...
January 19, 2018: Sensors
https://www.readbyqxmd.com/read/29351240/machine-learning-and-infrared-thermography-for-fiber-orientation-assessment-on-randomly-oriented-strands-parts
#14
Henrique Fernandes, Hai Zhang, Alisson Figueiredo, Fernando Malheiros, Luis Henrique Ignacio, Stefano Sfarra, Clemente Ibarra-Castanedo, Gilmar Guimaraes, Xavier Maldague
The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand...
January 19, 2018: Sensors
https://www.readbyqxmd.com/read/29351177/units-of-distinction-creating-a-blueprint-for-recognition-of-high-performing-medical-surgical-nursing-units
#15
Alvin D Jeffery, Sammie Mosier, Allison Baker, Kimberly Korwek, Cindy Borum, Jane Englebright
BACKGROUND: Hospital medical-surgical (M/S) nursing units are responsible for up to 28 million encounters annually, yet receive little attention from professional organizations and national initiatives targeted to improve quality and performance. OBJECTIVE: We sought to develop a framework recognizing high-performing units within our large hospital system. METHODS: This was a retrospective data analysis of M/S units throughout a 168-hospital system...
February 2018: Journal of Nursing Administration
https://www.readbyqxmd.com/read/29350398/use-of-computational-functional-genomics-in-drug-discovery-and-repurposing-for-analgesic-indications
#16
Jörn Lötsch, Dario Kringel
The novel research area of functional genomics investigates biochemical, cellular, or physiological properties of gene products with the goal of understanding the relationship between the genome and the phenotype. These developments have made analgesic drug research a data-rich discipline mastered only by making use of parallel developments in computer science, including the establishment of knowledge bases, mining methods for big data, machine-learning, and artificial intelligence, (Table ) which will be exemplarily introduced in the following...
January 19, 2018: Clinical Pharmacology and Therapeutics
https://www.readbyqxmd.com/read/29349899/collaborative-child-home-injury-prevention-in-thailand-an-action-research-study
#17
Alison I Machin, Amornrat Ngamsuoy, Pauline Pearson
Child home accidental injury is a global health issue, and promoting child safety is a pediatric nursing challenge worldwide. Planning child home accidental injury prevention requires understanding of factors influencing parents' behavior. Evidence suggests that participatory health promotion positively influences behavior; however, research on Thai parents is limited. This qualitative, action research study aimed to understand Thai parents' experiences of participating in a collaborative child home accidental injury prevention program and its influence on their behavior...
January 18, 2018: Nursing & Health Sciences
https://www.readbyqxmd.com/read/29349278/machine-learning-approaches-to-the-social-determinants-of-health-in-the-health-and-retirement-study
#18
Benjamin Seligman, Shripad Tuljapurkar, David Rehkopf
Background: Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. Methods: A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length...
April 2018: SSM—Population Health
https://www.readbyqxmd.com/read/29348779/mutual-proximity-graphs-for-improved-reachability-in-music-recommendation
#19
Arthur Flexer, Jeff Stevens
This paper is concerned with the impact of hubness, a general problem of machine learning in high-dimensional spaces, on a real-world music recommendation system based on visualisation of a k-nearest neighbour (knn) graph. Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists, resulting in poor reachability of the music catalogue. We present mutual proximity graphs, which are an alternative to knn and mutual knn graphs, and are able to avoid hub vertices having abnormally high connectivity...
2018: Journal of New Music Research
https://www.readbyqxmd.com/read/29348728/systems-level-mechanisms-of-action-of-panax-ginseng-a-network-pharmacological-approach
#20
REVIEW
Sa-Yoon Park, Ji-Hun Park, Hyo-Su Kim, Choong-Yeol Lee, Hae-Jeung Lee, Ki Sung Kang, Chang-Eop Kim
Panax ginseng has been used since ancient times based on the traditional Asian medicine theory and clinical experiences, and currently, is one of the most popular herbs in the world. To date, most of the studies concerning P. ginseng have focused on specific mechanisms of action of individual constituents. However, in spite of many studies on the molecular mechanisms of P. ginseng, it still remains unclear how multiple active ingredients of P. ginseng interact with multiple targets simultaneously, giving the multidimensional effects on various conditions and diseases...
January 2018: Journal of Ginseng Research
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