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https://www.readbyqxmd.com/read/29036627/using-uncertainty-to-link-and-rank-evidence-from-biomedical-literature-for-model-curation
#1
Chrysoula Zerva, Riza Batista-Navarro, Philip Day, Sophia Ananiadou
Motivation: In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author...
July 24, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036404/the-value-of-prior-knowledge-in-machine-learning-of-complex-network-systems
#2
Dana Ferranti, David Krane, David Craft
Motivation: Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available...
July 7, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29036277/pyseqlab-an-open-source-python-package-for-sequence-labeling-and-segmentation
#3
Ahmed Allam, Michael Krauthammer
Motivation: Text and genomic data are composed of sequential tokens, such as words and nucleotides that give rise to higher order syntactic constructs. In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data. Results: Python Sequence Labeling (PySeqLab) is an open source package for performing supervised learning in structured prediction tasks...
July 21, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29035295/nighttime-foreground-pedestrian-detection-based-on-three-dimensional-voxel-surface-model
#4
Jing Li, Fangbing Zhang, Lisong Wei, Tao Yang, Zhaoyang Lu
Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. To respond to this need, in this paper, we provide an affordable solution with a near-infrared stereo network camera, as well as a novel three-dimensional foreground pedestrian detection model...
October 16, 2017: Sensors
https://www.readbyqxmd.com/read/29034482/deep-reinforcement-learning-for-automated-radiation-adaptation-in-lung-cancer
#5
Huan-Hsin Tseng, Yi Luo, Sunan Cui, Jen-Tzung Chien, Randall K Ten Haken, Issam El Naqa
PURPOSE: To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for non-small cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2). METHODS: In a retrospective population of 114 NSCLC patients who received radiotherapy, a 3-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation...
October 16, 2017: Medical Physics
https://www.readbyqxmd.com/read/29033992/patient-safety-culture-in-intensive-care-units-from-the-perspective-of-nurses-a-cross-sectional-study
#6
Sedigheh Farzi, Azam Moladoost, Masoud Bahrami, Saba Farzi, Reza Etminani
BACKGROUND: One of the goals of nursing is providing safe care, prevention of injury, and health promotion of patients. Patient safety in intensive care units is threatened for various reasons. This study aimed to survey patient safety culture from the perspective of nurses in intensive care units. MATERIALS AND METHODS: This cross-sectional study was conducted in 2016. Sampling was done using the convenience method. The sample consisted of 367 nurses working in intensive care units of teaching hospitals affiliated to Isfahan University of Medical Sciences...
September 2017: Iranian Journal of Nursing and Midwifery Research
https://www.readbyqxmd.com/read/29033273/current-techniques-of-teaching-and-learning-in-bariatric-surgical-procedures-a-systematic-review
#7
Mirjam Kaijser, Gabrielle van Ramshorst, Bart van Wagensveld, Jean-Pierre Pierie
OBJECTIVE: The gastric sleeve resection and gastric bypass are the 2 most commonly performed bariatric procedures. This article provides an overview of current teaching and learning methods of those techniques in resident and fellow training. DESIGN: A database search was performed on Pubmed, Embase, and the Education Resources Information Center (ERIC) to identify the methods used to provide training in bariatric surgery worldwide. After exclusion based on titles and abstracts, full texts of the selected articles were assessed...
October 12, 2017: Journal of Surgical Education
https://www.readbyqxmd.com/read/29033006/relative-age-within-the-school-year-and-diagnosis-of-attention-deficit-hyperactivity-disorder-a-nationwide-population-based-study
#8
Kapil Sayal, Roshan Chudal, Susanna Hinkka-Yli-Salomäki, Petteri Joelsson, Andre Sourander
BACKGROUND: Findings are mixed on the relationship between attention-deficit hyperactivity disorder (ADHD) and younger relative age in the school year. We aimed to investigate whether relative age is associated with ADHD diagnosis in a country where prescribing rates are low and whether any such association has changed over time or relates to comorbid disorders (eg, conduct disorder [CD], oppositional defiant disorder [ODD], or learning disorder [LD]). METHODS: We used nationwide population-based registers to identify all Finnish children born between Jan 1, 1991, and Dec 31, 2004, who were diagnosed with ADHD from age 7 years onwards (age of starting school)...
October 9, 2017: Lancet Psychiatry
https://www.readbyqxmd.com/read/29032683/retrieving-the-quantitative-chemical-information-at-nanoscale-from-sem-edx-measurements-by-machine-learning
#9
Benedykt R Jany, Arkadiusz Janas, Franciszek Krok
The quantitative composition of metal alloy nanowires on InSb semiconductor surface and gold nanostructures on germanium surface is determined by blind source separation (BSS) machine learning (ML) method using non negative matrix factorization (NMF) from energy dispersive X-ray spectroscopy (EDX) spectrum image maps measured in a scanning electron microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum image into three source components, which correspond directly to the X-ray signals coming from the supported metal nanostructures, bulk semiconductor signal and carbon background...
October 15, 2017: Nano Letters
https://www.readbyqxmd.com/read/29032293/facilitating-problem-based-learning-among-undergraduate-nursing-students-a-qualitative-systematic-review
#10
REVIEW
Jacqueline Wosinski, Anne E Belcher, Yvan Dürrenberger, Anne-Claude Allin, Coraline Stormacq, Linda Gerson
OBJECTIVES: The purpose of this study was to identify and synthesize the best available evidence on the perspective of undergraduate nursing students on facilitating elements that contribute to their success with PBL. DESIGN: a qualitative systematic review of the literature according to meta-aggregative methodology using the JBI SUMARI system was conducted. DATA SOURCES: Data was collected across CINAHL, Medline, Embase, Eric, Teacher Reference Center and reference lists...
September 8, 2017: Nurse Education Today
https://www.readbyqxmd.com/read/29031966/bink-biological-binary-keypoint-descriptor
#11
Mário Saleiro, Kasim Terzić, J M F Rodrigues, J M H du Buf
Learning robust keypoint descriptors has become an active research area in the past decade. Matching local features is not only important for computational applications, but may also play an important role in early biological vision for disparity and motion processing. Although there were already some floating-point descriptors like SIFT and SURF that can yield high matching rates, the need for better and faster descriptors for real-time applications and embedded devices with low computational power led to the development of binary descriptors, which are usually much faster to compute and to match...
October 12, 2017: Bio Systems
https://www.readbyqxmd.com/read/29031664/nonlinearity-aware-based-dimensionality-reduction-and-over-sampling-for-ad-mci-classification-from-mri-measures
#12
Peng Cao, Xiaoli Liu, Jinzhu Yang, Dazhe Zhao, Min Huang, Jian Zhang, Osmar Zaiane
Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming more and more critical and emphasized at the earliest stages. However, the high dimensionality and imbalanced data issues are two major challenges in the study of computer aided AD diagnosis. The greatest limitations of existing dimensionality reduction and over-sampling methods are that they assume a linear relationship between the MRI features (predictor) and the disease status (response)...
October 6, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29030801/deep-arm-ear-ecg-image-learning-for-highly-wearable-biometric-human-identification
#13
Qingxue Zhang, Dian Zhou
In this study, to advance smart health applications which have increasing security/privacy requirements, we propose a novel highly wearable ECG-based user identification system, empowered by both non-standard convenient ECG lead configurations and deep learning techniques. Specifically, to achieve a super wearability, we suggest situating all the ECG electrodes on the left upper-arm, or behind the ears, and successfully obtain weak but distinguishable ECG waveforms. Afterwards, to identify individuals from weak ECG, we further present a two-stage framework, including ECG imaging and deep feature learning/identification...
October 13, 2017: Annals of Biomedical Engineering
https://www.readbyqxmd.com/read/29029695/continued-use-of-an-interactive-computer-game-based-visual-perception-learning-system-in-children-with-developmental-delay
#14
Hsien-Cheng Lin, Yu-Hsien Chiu, Yenming J Chen, Yee-Pay Wuang, Chiu-Ping Chen, Chih-Chung Wang, Chien-Ling Huang, Tang-Meng Wu, Wen-Hsien Ho
This study developed an interactive computer game-based visual perception learning system for special education children with developmental delay. To investigate whether perceived interactivity affects continued use of the system, this study developed a theoretical model of the process in which learners decide whether to continue using an interactive computer game-based visual perception learning system. The technology acceptance model, which considers perceived ease of use, perceived usefulness, and perceived playfulness, was extended by integrating perceived interaction (i...
November 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29028197/multi-task-vehicle-detection-with-region-of-interest-voting
#15
Wenqing Chu, Yao Liu, Chen Shen, Deng Cai, Xian-Sheng Hua
Vehicle detection is a challenging problem in autonomous driving systems, due to its large structural and appearance variations. In this paper, we propose a novel vehicle detection scheme based on multi-task deep convolutional neural networks (CNN) and region-of-interest (RoI) voting. In the design of CNN architecture, we enrich the supervised information with subcategory, region overlap, bounding-box regression and category of each training RoI as a multi-task learning framework. This design allows the CNN model to share visual knowledge among different vehicle attributes simultaneously, thus detection robustness can be effectively improved...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29028191/deep-neural-networks-for-no-reference-and-full-reference-image-quality-assessment
#16
Sebastian Bosse, Dominique Maniry, Klaus-Robert Muller, Thomas Wiegand, Wojciech Samek
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained endto- end and comprises 10 convolutional layers and 5 pooling layers for feature extraction, and 2 fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that (1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and (2) it allows for joint learning of local quality and local weights, i...
October 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29025427/adaptive-hybrid-robotic-system-for-rehabilitation-of-reaching-movement-after-a-brain-injury-a-usability-study
#17
F Resquín, J Gonzalez-Vargas, J Ibáñez, F Brunetti, I Dimbwadyo, L Carrasco, S Alves, C Gonzalez-Alted, A Gomez-Blanco, J L Pons
BACKGROUND: Brain injury survivors often present upper-limb motor impairment affecting the execution of functional activities such as reaching. A currently active research line seeking to maximize upper-limb motor recovery after a brain injury, deals with the combined use of functional electrical stimulation (FES) and mechanical supporting devices, in what has been previously termed hybrid robotic systems. This study evaluates from the technical and clinical perspectives the usability of an integrated hybrid robotic system for the rehabilitation of upper-limb reaching movements after a brain lesion affecting the motor function...
October 12, 2017: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/29025363/identification-of-core-objectives-for-teaching-sustainable-healthcare-education
#18
Arianne Teherani, Holly Nishimura, Latifat Apatira, Thomas Newman, Susan Ryan
BACKGROUND: Physicians will be called upon to care for patients who bear the burden of disease from the impact of climate change and ecologically irresponsible practices which harm ecosystems and contribute to climate change. However, physicians must recognize the connection between the climate, ecosystems, sustainability, and health and their responsibility and capacity in changing the status quo. Sustainable healthcare education (SHE), defined as education about the impact of climate change and ecosystem alterations on health and the impact of the healthcare industry on the aforementioned, is vital to prevention of adverse health outcomes due to the changing climate and environment...
2017: Medical Education Online
https://www.readbyqxmd.com/read/29022403/developmental-approach-for-behavior-learning-using-primitive-motion-skills
#19
Farhan Dawood, Chu Kiong Loo
Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration...
August 6, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29021930/computer-assisted-learning-applications-in-health-educational-informatics-a-review
#20
REVIEW
Faiq Shaikh, Faisal Inayat, Omer Awan, Marlise D Santos, Adnan M Choudhry, Abdul Waheed, Dilkash Kajal, Sagun Tuli
Computer-assisted learning (CAL) as a health informatics application is a useful tool for medical students in the era of expansive knowledge bases and the increasing need for and the consumption of automated and interactive systems. As the scope and breadth of medical knowledge expand, the need for additional learning outside of lecture hours is becoming increasingly important. CAL can be an impactful adjunct to conventional methods that currently exist in the halls of learning. There is an increasing body of literature that suggests that CAL should be a commonplace and the recommended method of learning for medical students...
August 10, 2017: Curēus
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