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https://www.readbyqxmd.com/read/28644814/learning-multimodal-parameters-a-bare-bones-niching-differential-evolution-approach
#1
Yue-Jiao Gong, Jun Zhang, Yicong Zhou
Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning...
June 20, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28644300/breathlessness-in-the-primary-care-setting
#2
Noel Baxter
PURPOSE OF REVIEW: Breathlessness is a high-volume problem with 10% of adults experiencing the symptom daily placing a heavy burden on the health and wider economy. As it worsens, they enter the specialist and hospital-based symptom services where costs quickly escalate and people may find themselves in a place not of their choosing. For many, their care will be delivered by a disease or organ specialist and can find themselves passing between physicians without coordination for symptom support...
July 20, 2017: Current Opinion in Supportive and Palliative Care
https://www.readbyqxmd.com/read/28643300/-advantages-and-disadvantages-of-minimally-invasive-surgery-in-colorectal-cancer-surgery
#3
Minhua Zheng, Junjun Ma
Since the emergence of minimally invasive technology twenty years ago, as a surgical concept and surgical technique for colorectal cancer surgery, its obvious advantages have been recognized. Laparoscopic technology, as one of the most important technology platform, has got a lot of evidence-based support for the oncological safety and effectiveness in colorectal cancer surgery Laparoscopic technique has advantages in terms of identification of anatomic plane and autonomic nerve, protection of pelvic structure, and fine dissection of vessels...
June 25, 2017: Zhonghua Wei Chang Wai Ke za Zhi, Chinese Journal of Gastrointestinal Surgery
https://www.readbyqxmd.com/read/28643174/natural-language-processing-for-ehr-based-pharmacovigilance-a-structured-review
#4
REVIEW
Yuan Luo, William K Thompson, Timothy M Herr, Zexian Zeng, Mark A Berendsen, Siddhartha R Jonnalagadda, Matthew B Carson, Justin Starren
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions...
June 22, 2017: Drug Safety: An International Journal of Medical Toxicology and Drug Experience
https://www.readbyqxmd.com/read/28642871/promoting-mental-health-in-italian-middle-and-high-school-a-pilot-study
#5
Franco Veltro, Valentina Ialenti, Manuel Alejandro Morales García, Emiliana Bonanni, Claudia Iannone, Marinella D'Innocenzo, Antonella Gigantesco
AIM: In Italy, a handbook has been developed based on the principles of cooperative learning, life skills, self-effectiveness, and problem-solving at high school level. Early studies have shown the handbook's effectiveness. It has been hypothesized that the revised handbook could be more effective in middle schools. METHOD: The study design is a "pre- and posttest" that compares the results obtained from 91 students of the high schools with those of the 38 students from middle schools...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28641555/supervised-machine-learning-methods-applied-to-predict-ligand-binding-affinity
#6
Gabriela Sehnem Heck, Val Oliveira Pintro, Richard Rene Pereira, Mauricio Boff de Ávila, Nayara Maria Bernhardt Levin, Walter Filgueira de Azevedo
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. OBJECTIVE: Our goal here is to review recent developments in the application of machine learning methods to predict ligand- binding affinity...
June 22, 2017: Current Medicinal Chemistry
https://www.readbyqxmd.com/read/28641262/scalable-multi-view-semi-supervised-classification-via-adaptive-regression
#7
Hong Tao, Chenping Hou, Feiping Nie, Jubo Zhu, Dongyun Yi
With the advent of multi-view data, multi-view learning has become an important research direction in machine learning and image processing. Considering the difficulty of obtaining labeled data in many machine learning applications, we focus on the multi-view semi-supervised classification problem. In this paper, we propose an algorithm named Multi-View Semi-Supervised Classification via Adaptive Regression (MVAR) to address this problem. Specifically, regression based loss functions with ℓ2,1 matrix norm are adopted for each view and the final objective function is formulated as the linear weighted combination of all the loss functions...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641250/deep-convolutional-neural-network-for-inverse-problems-in-imaging
#8
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, Michael Unser
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution...
June 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641247/modeling-task-fmri-data-via-deep-convolutional-autoencoder
#9
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28641239/automatic-recognition-of-fmri-derived-functional-networks-using-3d-convolutional-neural-networks
#10
Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications...
June 15, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28641208/an-associative-account-of-the-development-of-word-learning
#11
Vladimir M Sloutsky, Hyungwook Yim, Xin Yao, Simon Dennis
Word learning is a notoriously difficult induction problem because meaning is underdetermined by positive examples. How do children solve this problem? Some have argued that word learning is achieved by means of inference: young word learners rely on a number of assumptions that reduce the overall hypothesis space by favoring some meanings over others. However, these approaches have difficulty explaining how words are learned from conversations or text, without pointing or explicit instruction. In this research, we propose an associative mechanism that can account for such learning...
June 19, 2017: Cognitive Psychology
https://www.readbyqxmd.com/read/28635623/compressed-sensing-reconstruction-based-on-block-sparse-bayesian-learning-in-bearing-condition-monitoring
#12
Jiedi Sun, Yang Yu, Jiangtao Wen
Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression only, cannot overcome these limitations. Aiming at these problems, this paper proposed a compressed data acquisition and reconstruction scheme based on Compressed Sensing (CS) which is a novel signal-processing technique and applied it for bearing conditions monitoring via WSN...
June 21, 2017: Sensors
https://www.readbyqxmd.com/read/28635122/sketching-the-invisible-to-predict-the-visible-from-drawing-to-modeling-in-chemistry
#13
Melanie M Cooper, Mike Stieff, Dane DeSutter
Sketching as a scientific practice goes beyond the simple act of inscribing diagrams onto paper. Scientists produce a wide range of representations through sketching, as it is tightly coupled to model-based reasoning. Chemists in particular make extensive use of sketches to reason about chemical phenomena and to communicate their ideas. However, the chemical sciences have a unique problem in that chemists deal with the unseen world of the atomic-molecular level. Using sketches, chemists strive to develop causal mechanisms that emerge from the structure and behavior of molecular-level entities, to explain observations of the macroscopic visible world...
June 21, 2017: Topics in Cognitive Science
https://www.readbyqxmd.com/read/28634789/detection-and-grading-of-prostate-cancer-using-temporal-enhanced-ultrasound-combining-deep-neural-networks-and-tissue-mimicking-simulations
#14
Shekoofeh Azizi, Sharareh Bayat, Pingkun Yan, Amir Tahmasebi, Guy Nir, Jin Tae Kwak, Sheng Xu, Storey Wilson, Kenneth A Iczkowski, M Scott Lucia, Larry Goldenberg, Septimiu E Salcudean, Peter A Pinto, Bradford Wood, Purang Abolmaesumi, Parvin Mousavi
PURPOSE  : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS  : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core...
June 20, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28634344/omniga-optimized-omnivariate-decision-trees-for-generalizable-classification-models
#15
Arturo Magana-Mora, Vladimir B Bajic
Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods...
June 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28633551/a-pareto-based-ensemble-with-feature-and-instance-selection-for-learning-from-multi-class-imbalanced-datasets
#16
Alberto Fernández, Cristobal José Carmona, María José Del Jesus, Francisco Herrera
Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes...
April 11, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28632009/self-observation-and-peer-feedback-as-a-faculty-development-approach-for-problem-based-learning-tutors-a-program-evaluation
#17
Irène Garcia, Richard W James, Paul Bischof, Anne Baroffio
PROBLEM: Good teaching requires spontaneous, immediate, and appropriate action in response to various situations. It is even more crucial in problem-based learning (PBL) tutorials, as the tutors, while directing students toward the identification and attainment of learning objectives, must stimulate them to contribute to the process and provide them with constructive feedback. PBL tutors in medicine lack opportunities to receive feedback from their peers on their teaching strategies. Moreover, as tutorials provide little or no time to stop and think, more could be learned by reflecting on the experience than from the experience itself...
March 2, 2017: Teaching and Learning in Medicine
https://www.readbyqxmd.com/read/28631234/quality-matters-implementation-moderates-student-outcomes-in-the-paths-curriculum
#18
Neil Humphrey, Alexandra Barlow, Ann Lendrum
Analyses of the relationship between levels of implementation and outcomes of school-based social and emotional learning (SEL) interventions are relatively infrequent and are typically narrowly focused. Thus, our objective was to assess the relationship between variability in a range of implementation dimensions and intervention outcomes in the Promoting Alternative Thinking Strategies (PATHS) curriculum. Implementation of PATHS was examined in 69 classrooms across 23 schools in the first year of a major randomized controlled trial...
June 19, 2017: Prevention Science: the Official Journal of the Society for Prevention Research
https://www.readbyqxmd.com/read/28628367/learning-to-spell-phonology-and-beyond
#19
Rebecca Treiman
An understanding of the nature of writing systems and of the typical course of spelling development is an essential foundation for understanding the problems of children who have serious difficulties in learning to spell. The present article seeks to provide that foundation. It argues that the dual-route models of spelling that underlie much existing research and practice are based on overly simple assumptions about how writing systems work and about how spelling skills develop. Many writing systems include not only context-free links from phonemes to letters but also context-sensitive phonological patterns, morphological influences, and graphotactic patterns...
June 19, 2017: Cognitive Neuropsychology
https://www.readbyqxmd.com/read/28627920/reliability-and-validity-of-a-spanish-language-assessment-of-children-s-social-emotional-learning-skills
#20
Jaclyn M Russo, Clark McKown, Nicole M Russo-Ponsaran, Adelaide Allen
Few Spanish language tools are available for assessing important social-emotional learning (SEL) skills. The present study presents evidence of the psychometric properties of a Spanish-language version of SELweb (SELweb-S), a web-based system for assessing children's ability to recognize others' emotions and perspectives, solve social problems, and engage in self-control. With a sample of 524 students in Grades K to 3, we examined the reliability and validity of SELweb-S. This study provided evidence that (a) individual assessment modules exhibited moderate to high internal consistency and moderate 6-month temporal stability, (b) composite assessment scores exhibited high reliability, (c) assessment module scores fit a theoretically coherent factor structure, and (d) performance on SELweb-S assessment modules was positively related to teacher-reported SEL skills...
June 19, 2017: Psychological Assessment
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