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learning analytics

Enrique Castro-Sánchez, Yiannis Kyratsis, Michiyo Iwami, Timothy M Rawson, Alison H Holmes
BACKGROUND: The uptake of improvement initiatives in infection prevention and control (IPC) has often proven challenging. Innovative interventions such as 'serious games' have been proposed in other areas to educate and help clinicians adopt optimal behaviours. There is limited evidence about the application and evaluation of serious games in IPC. The purposes of the study were: a) to synthesise research evidence on the use of serious games in IPC to support healthcare workers' behaviour change and best practice learning; and b) to identify gaps across the formulation and evaluation of serious games in IPC...
2016: Antimicrobial Resistance and Infection Control
Elena Sokolova, Perry Groot, Tom Claassen, Kimm J van Hulzen, Jeffrey C Glennon, Barbara Franke, Tom Heskes, Jan Buitelaar
BACKGROUND: Numerous factor analytic studies consistently support a distinction between two symptom domains of attention-deficit/hyperactivity disorder (ADHD), inattention and hyperactivity/impulsivity. Both dimensions show high internal consistency and moderate to strong correlations with each other. However, it is not clear what drives this strong correlation. The aim of this paper is to address this issue. METHOD: We applied a sophisticated approach for causal discovery on three independent data sets of scores of the two ADHD dimensions in NeuroIMAGE (total N = 675), ADHD-200 (N = 245), and IMpACT (N = 164), assessed by different raters and instruments, and further used information on gender or a genetic risk haplotype...
2016: PloS One
Zhiwei Zhou, Xiaotao Shen, Jia Tu, Zheng-Jiang Zhu
The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility - mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites...
October 21, 2016: Analytical Chemistry
Alistair E W Johnson, Mohammad M Ghassemi, Shamim Nemati, Katherine E Niehaus, David A Clifton, Gari D Clifford
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms...
February 2016: Proceedings of the IEEE
Yoonsik Shim, Andrew Philippides, Kevin Staras, Phil Husbands
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events...
October 2016: PLoS Computational Biology
Nicholas K Schiltz, David F Warner, Jiayang Sun, Paul M Bakaki, Avi Dor, Charles W Given, Kurt C Stange, Siran M Koroukian
BACKGROUND: Multimorbidity affects the majority of elderly adults and is associated with higher health costs and utilization, but how specific patterns of morbidity influence resource use is less understood. OBJECTIVE: The objective was to identify specific combinations of chronic conditions, functional limitations, and geriatric syndromes associated with direct medical costs and inpatient utilization. DESIGN: Retrospective cohort study using the Health and Retirement Study (2008-2010) linked to Medicare claims...
October 6, 2016: Medical Care
David R Shanks
Many studies of unconscious processing involve comparing a performance measure (e.g., some assessment of perception or memory) with an awareness measure (such as a verbal report or a forced-choice response) taken either concurrently or separately. Unconscious processing is inferred when above-chance performance is combined with null awareness. Often, however, aggregate awareness is better than chance, and data analysis therefore employs a form of extreme group analysis focusing post hoc on participants, trials, or items where awareness is absent or at chance...
October 17, 2016: Psychonomic Bulletin & Review
Brent C Pottenger, Richard O Davis, Joanne Miller, Lisa Allen, Melinda Sawyer, Peter J Pronovost
OBJECTIVE: To determine whether Comprehensive Unit-based Safety Program (CUSP) teams could be used to enhance patient experience by improving care transitions and discharge processes in a 318-bed community hospital. METHODS: In 2015, CUSP teams produced feasible solutions by participating in a design-thinking initiative, coupled with performance improvement tools involving data analytics and peer-learning communities. Teams completed a 90-day sprint challenge, involving weekly meetings, monthly department leader meetings, and progress trackers...
October 2016: Quality Management in Health Care
Sara Boucher, Olivia Edwards, Andrew Gray, Shyamala Nada-Raja, Jason Lillis, Tracy L Tylka, Caroline C Horwath
BACKGROUND: Middle-aged women are at risk of weight gain and associated comorbidities. Deliberate restriction of food intake (dieting) produces short-term weight loss but is largely unsuccessful for long-term weight management. Two promising approaches for the prevention of weight gain are intuitive eating (ie, eating in accordance with hunger and satiety signals) and the development of greater psychological flexibility (ie, the aim of acceptance and commitment therapy [ACT]). OBJECTIVES: This pilot study investigated the usage, acceptability, and feasibility of "Mind, Body, Food," a Web-based weight gain prevention intervention prototype that teaches intuitive eating and psychological flexibility skills...
October 14, 2016: JMIR Research Protocols
Jingyang Jiang, Jinghui Ouyang, Haitao Liu
Language is not only the representation of thinking, but also shapes thinking. Studies on bilinguals suggest that a foreign language plays an important and unconscious role in thinking. In this study, a software-Linguistic Inquiry and Word Count 2007-was used to investigate whether the learning of English as a foreign language (EFL) can foster Chinese high school students' English analytic thinking (EAT) through the analysis of their English writings with our self-built corpus. It was found that: (1) learning English can foster Chinese learners' EAT...
2016: PloS One
Lei Zhang, David Zhang
Conventional extreme learning machines (ELMs) solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition-based access control system, where misclassifying a stranger as a family member may result in more serious disaster than misclassifying a family member as a stranger...
October 11, 2016: IEEE Transactions on Neural Networks and Learning Systems
Jianlei Zhang, Franz J Weissing, Ming Cao
A commonly used assumption in evolutionary game theory is that natural selection acts on individuals in the same time scale; e.g., players use the same frequency to update their strategies. Variation in learning rates within populations suggests that evolutionary game theory may not necessarily be restricted to uniform time scales associated with the game interaction and strategy adaption evolution. In this study, we remove this restricting assumption by dividing the population into fast and slow groups according to the players' strategy updating frequencies and investigate how different strategy compositions of one group influence the evolutionary outcome of the other's fixation probabilities of strategies within its own group...
September 2016: Physical Review. E
Nicolas Courty, Remi Flamary, Devis Tuia, Alain Rakotomamonjy
Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains...
October 7, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Ruchi R Shah, Kimberly J Hassett, Luis A Brito
Adjuvants are included in sub-unit or recombinant vaccines to enhance the potency of poorly immunogenic antigens. Adjuvant discovery is as complex as it is a multidiscplinary intersection of formulation science, immunology, toxicology, and biology. Adjuvants such as alum, which have been in use for the past 90 years, have illustrated that adjuvant research is a methodical process. As science advances, new analytical tools are developed which allows us to delve deeper into the various mechanisms that generates a potent immune response...
2017: Methods in Molecular Biology
Matthew S Delfiner, Luis R Martinez, Charles S Pavia
BACKGROUND: Laboratory diagnostic tests have an essential role in patient care, and the increasing number of medical and health professions schools focusing on teaching laboratory medicine to pre-clinical students reflects this importance. However, data validating the pedagogical methods that best influence students' comprehension and interpretation of diagnostic tests have not been well described. The Gram stain is a simple yet significant and frequently used diagnostic test in the clinical setting that helps classify bacteria into two major groups, Gram positive and negative, based on their cell wall structure...
2016: PloS One
J Hara, W R Shankle, L W Barrentine, M V Curole
OBJECTIVES: Studies have produced conflicting results assessing hyperhomocysteinemia (HYH) treatment with B vitamins in patients with normal cognition, Alzheimer's disease and related disorders (ADRD). This study examined the effect of HYH management with L-methylfolate (LMF), methylcobalamin (MeCbl; B12), and N-acetyl-cysteine (CFLN: Cerefolin®/Cerefolin-NAC®) on cognitive decline. DESIGN: Prospective, case-control study of subjects followed longitudinally. SETTING: Outpatient clinic for cognitive disorders...
2016: Journal of Nutrition, Health & Aging
Saul Blecker, Stuart D Katz, Leora I Horwitz, Gilad Kuperman, Hannah Park, Alex Gold, David Sontag
Importance: Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Objective: To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital...
October 5, 2016: JAMA Cardiology
Seyde Shahrbanoo Daniali, Hossein Shahnazi, Samira Kazemi, Elnaz Marzbani
INTRODUCTION: Multiple Sclerosis (MS) is one of the most common autoimmune diseases affecting the central nervous system. The prevalence of it is increasing in our country too. The pain from disorders can affect quality of life. Several studies have pointed to the improvement of patients through educational intervention. This study attempted to evaluate the effectiveness of an educational intervention based on raising the awareness and self-efficacy for pain control among patients with multiple sclerosis during 2015 under the coverage of Isfahan MS Society (IMSS)...
July 24, 2016: Materia Socio-medica
Peter V Coveney, Edward R Dougherty, Roger R Highfield
The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales...
November 13, 2016: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
Daohui Zeng, Jidong Peng, Simon Fong, Yining Qiu, Raymond Wong, Yi-Jen Mon
Sentiment prediction emerged as an important machine learning topic to gain insights from unstructured texts, recently gained popularity in health-care industries. Text mining has long been a fundamental data analytic for sentiment prediction. A popular pre-processing step in text mining is transforming text strings to word vectors which form a high-dimensional sparse matrix. This sparse matrix poses computational challenges to induction of accurate sentiment prediction model. Feature selection has been a popular dimensionality reduction technique that finds a subset of features from all the original features from the sparse matrix, in order to enhance the accuracy of the prediction model...
August 5, 2016: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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