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https://www.readbyqxmd.com/read/29149385/pre-clinical-models-in-pediatric-traumatic-brain-injury-challenges-and-lessons-learned
#1
REVIEW
Patrick M Kochanek, Jessica S Wallisch, Hülya Bayır, Robert S B Clark
PURPOSE: Despite the enormity of the problem and the lack of new therapies, research in the pre-clinical arena specifically using pediatric traumatic brain injury (TBI) models is limited. In this review, some of the key models addressing both the age spectrum of pediatric TBI and its unique injury mechanisms will be highlighted. Four topics will be addressed, namely, (1) unique facets of the developing brain important to TBI model development, (2) a description of some of the most commonly used pre-clinical models of severe pediatric TBI including work in both rodents and large animals, (3) a description of the pediatric models of mild TBI and repetitive mild TBI that are relatively new, and finally (4) a discussion of challenges, gaps, and potential future directions to further advance work in pediatric TBI models...
October 2017: Child's Nervous System: ChNS: Official Journal of the International Society for Pediatric Neurosurgery
https://www.readbyqxmd.com/read/29147562/cognitive-computing-and-escience-in-health-and-life-science-research-artificial-intelligence-and-obesity-intervention-programs
#2
REVIEW
Thomas Marshall, Tiffiany Champagne-Langabeer, Darla Castelli, Deanna Hoelscher
Objective: To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. Methods: The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29145413/diffusion-based-neuromodulation-can-eliminate-catastrophic-forgetting-in-simple-neural-networks
#3
Roby Velez, Jeff Clune
A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules)...
2017: PloS One
https://www.readbyqxmd.com/read/29145086/active-learning-for-classifying-data-streams-with-unknown-number-of-classes
#4
Saad Mohamad, Moamar Sayed-Mouchaweh, Abdelhamid Bouchachia
The classification of data streams is an interesting but also a challenging problem. A data stream may grow infinitely making it impractical for storage prior to processing and classification. Due to its dynamic nature, the underlying distribution of the data stream may change over time resulting in the so-called concept drift or the possible emergence and fading of classes, known as concept evolution. In addition, acquiring labels of data samples in a stream is admittedly expensive if not infeasible at all...
October 27, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29138614/development-of-a-health-care-systems-curriculum
#5
Zachary Pruitt, Rahul Mhaskar, Bryan G Kane, Robert D Barraco, Deborah J DeWaay, Alex M Rosenau, Kristin A Bresnan, Marna Rayl Greenberg
Background: There is currently no gold standard for delivery of systems-based practice in medical education, and it is challenging to incorporate into medical education. Health systems competence requires physicians to understand patient care within the broader health care system and is vital to improving the quality of care clinicians provide. We describe a health systems curriculum that utilizes problem-based learning across 4 years of systems-based practice medical education at a single institution...
2017: Advances in Medical Education and Practice
https://www.readbyqxmd.com/read/29137838/fusion-of-fmri-and-non-imaging-data-for-adhd-classification
#6
Atif Riaz, Muhammad Asad, Eduardo Alonso, Greg Slabaugh
Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of different brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behavior analysis. This paper addresses the problem of classification of ADHD based on resting state fMRI and proposes a machine learning framework with integration of non-imaging data with imaging data to investigate functional connectivity alterations between ADHD and control subjects (not diagnosed with ADHD)...
October 19, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29137603/clustertad-an-unsupervised-machine-learning-approach-to-detecting-topologically-associated-domains-of-chromosomes-from-hi-c-data
#7
Oluwatosin Oluwadare, Jianlin Cheng
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts...
November 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29136282/principal-component-reconstruction-pcr-for-cine-cbct-with-motion-learning-from-2d-fluoroscopy
#8
Hao Gao, Yawei Zhang, Lei Ren, Fang-Fang Yin
PURPOSE: This work aims to generate cine CT images (i.e., 4D images with high temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. METHODS: In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections...
November 14, 2017: Medical Physics
https://www.readbyqxmd.com/read/29132626/early-hospital-mortality-prediction-of-intensive-care-unit-patients-using-an-ensemble-learning-approach
#9
Aya Awad, Mohamed Bader-El-Den, James McNicholas, Jim Briggs
BACKGROUND: Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission...
December 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29131878/association-of-brain-structure-changes-and-cognitive-function-with-combination-antiretroviral-therapy-in-hiv-positive-individuals
#10
Ryan Sanford, Lesley K Fellows, Beau M Ances, D Louis Collins
Importance: Despite the introduction of combination antiretroviral therapy (cART), HIV-associated neurocognitive disorders continue to be a problem for treated HIV-positive individuals. The cause of this impairment remains unclear. Objective: To determine if detectable brain changes occur during a 2-year period in HIV-positive individuals who were aviremic and treated with cART. Design, Setting, and Participants: In this longitudinal case-control study, participants underwent neuroimaging and neuropsychological assessment approximately 2 years apart...
November 13, 2017: JAMA Neurology
https://www.readbyqxmd.com/read/29130300/psite-amino-acid-confidence-evaluation-for-quality-control-of-de-novo-peptide-sequencing-and-modification-site-localization
#11
Hao Yang, Hao Chi, Wen-Jing Zhou, Wen-Feng Zeng, Chao Liu, Rui-Min Wang, Zhao-Wei Wang, Xiu-Nan Niu, Zhen-Lin Chen, Si-Min He
MS-based de novo peptide sequencing has been improved remarkably with significant development of mass spectrometry and computational approaches, but still lacks quality control methods. Here we proposed a novel algorithm pSite to evaluate the confidence of each amino acid rather than the full-length peptides obtained by de novo peptide sequencing. A semi-supervised learning approach was used to discriminate correct amino acids from random ones and then an expectation-maximization algorithm was used to adaptively control the false amino-acid rate (FAR)...
November 13, 2017: Journal of Proteome Research
https://www.readbyqxmd.com/read/29127808/improving-autocoding-performance-of-rare-categories-in-injury-classification-is-more-training-data-or-filtering-the-solution
#12
Gaurav Nanda, Kirsten Vallmuur, Mark Lehto
INTRODUCTION: Classical Machine Learning (ML) models have been found to assign the external-cause-of-injury codes (E-codes) based on injury narratives with good overall accuracy but often struggle with rare categories, primarily due to lack of enough training cases and heavily skewed nature of injurdata. In this paper, we have: a) studied the effect of increasing the size of training data on the prediction performance of three classical ML models: Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM) and Logistic Regression (LR), and b) studied the effect of filtering based on prediction strength of LR model when the model is trained on very-small (10,000 cases) and very-large (450,000 cases) training sets...
November 8, 2017: Accident; Analysis and Prevention
https://www.readbyqxmd.com/read/29127673/technology-development-as-a-normative-practice-a-meaning-based-approach-to-learning-about-values-in-engineering-damming-as-a-case-study
#13
Mahdi G Nia, Mehdi F Harandi, Marc J de Vries
Engineering, as a complex and multidimensional practice of technology development, has long been a source of ethical concerns. These concerns have been approached from various perspectives. There are ongoing debates in the literature of the philosophy of engineering/technology about how to organize an optimized view of the values entailed in technology development processes. However, these debates deliver little in the way of a concrete rationale or framework that could comprehensively describe different types of engineering values and their multi-aspect interrelations in real engineering practices...
November 10, 2017: Science and Engineering Ethics
https://www.readbyqxmd.com/read/29127581/lessons-learned-in-induced-fit-docking-and-metadynamics-in-the-drug-design-data-resource-grand-challenge-2
#14
Matthew P Baumgartner, David A Evans
Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds...
November 10, 2017: Journal of Computer-aided Molecular Design
https://www.readbyqxmd.com/read/29126852/a-three-step-health-services-research-approach-to-improve-prescribing
#15
REVIEW
Adam J Rose, Megan B McCullough, Guneet K Jasuja
Medications are often prescribed suboptimally; some effective medications are underused, some ineffective medications are overused, and some medications that should be received by a few are instead given to many. The underlying causes of suboptimal prescribing likely differ for each medication, and therefore must be understood anew, although previous studies can help generate hypotheses. This perspective sets forth a 3-step research agenda, which has worked well for us in several recently completed and ongoing projects...
November 7, 2017: Healthcare
https://www.readbyqxmd.com/read/29126580/codebook-based-electrooculography-data-analysis-towards-cognitive-activity-recognition
#16
P Lagodzinski, K Shirahama, M Grzegorzek
With the advancement in mobile/wearable technology, people started to use a variety of sensing devices to track their daily activities as well as health and fitness conditions in order to improve the quality of life. This work addresses an idea of eye movement analysis, which due to the strong correlation with cognitive tasks can be successfully utilized in activity recognition. Eye movements are recorded using an electrooculographic (EOG) system built into the frames of glasses, which can be worn more unobtrusively and comfortably than other devices...
October 28, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29121903/diagnostic-errors-by-medical-students-results-of-a-prospective-qualitative-study
#17
Leah T Braun, Laura Zwaan, Jan Kiesewetter, Martin R Fischer, Ralf Schmidmaier
BACKGROUND: Diagnostic errors occur frequently in daily clinical practice and put patients' safety at risk. There is an urgent need to improve education on clinical reasoning to reduce diagnostic errors. However, little is known about diagnostic errors of medical students. In this study, the nature of the causes of diagnostic errors made by medical students was analyzed. METHODS: In June 2016, 88 medical students worked on eight cases with the chief complaint dyspnea in a laboratory setting using an electronic learning platform, in summary 704 processed cases...
November 9, 2017: BMC Medical Education
https://www.readbyqxmd.com/read/29121541/development-of-a-reinforcement-learning-based-evolutionary-fuzzy-rule-based-system-for-diabetes-diagnosis
#18
Fatemeh Mansourypoor, Shahrokh Asadi
The early diagnosis of disease is critical to preventing the occurrence of severe complications. Diabetes is a serious health problem. A variety of methods have been developed for diagnosing diabetes. The majority of these methods have been developed in a black-box manner, which cannot be used to explain the inference and diagnosis procedure. Therefore, it is essential to develop methods with high accuracy and interpretability. In this study, a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System (RLEFRBS) is developed for diabetes diagnosis...
October 31, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29120912/effects-of-a-system-thinking-based-simulation-program-for-congestive-heart-failure
#19
Hyeon-Young Kim, Eun Kyoung Yun
This study evaluated a system thinking-based simulation program for the care of patients with congestive heart failure. Participants were 67 undergraduate nursing students from a nursing college in Seoul, South Korea. The experimental group was given a 4-hour system-thinking program and a 2-hour simulation program, whereas the control group had a 4-hour case study and a 2-hour simulation program. There were significant improvements in critical thinking in both groups, but no significant group differences between educational methods (F = 3...
November 8, 2017: Computers, Informatics, Nursing: CIN
https://www.readbyqxmd.com/read/29120842/do-cognitive-deficits-predict-negative-emotionality-and-aggression-in-schizophrenia
#20
Anthony O Ahmed, Jenae Richardson, Alex Buckner, Sabrina Romanoff, Michelle Feder, Njideka Oragunye, Andriana Ilnicki, Ishrat Bhat, Matthew J Hoptman, Jean-Pierre Lindenmayer
Schizophrenia is associated with an elevated risk of aggression. Cognitive deficits have been associated with inpatient aggression and future violence. The relationship between cognitive deficits and violent behavior has however been inconsistent across studies. In addition, studies have failed to inform how cognitive deficits may contribute to aggression in schizophrenia. The current study examined the association of cognitive deficits with schizophrenia-related aggression and violent offending. It also explored the putative mediating role of negative emotionality on the impact of cognitive deficits on aggression...
November 7, 2017: Psychiatry Research
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