One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC-ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for the classification of non-convex 2 and 3-dimensional figures. An extensive benchmarking of our new FULL HYBRID classifiers against existing quantum and classical classifier models, reveals that our novel models exhibit better learning characteristics to asymmetrical Gaussian noise in the dataset compared to known quantum classifiers and performs equally well for existing classical classifiers, with a slight improvement over classical results in the region of the high noise.

Recent outbreaks and renewed concerns about immunization coverage call for new and effective interventions to improve vaccine uptake. Digital technologies have the potential to help address both suboptimal vaccine uptake and series completion. However, the effectiveness of pushing information and reminders to patients through digital technologies to address vaccination is not known.
The aim of this study is to determine if digital push interventions are effective in increasing vaccine uptake and series completion compared to non-digital interventions.
We searched for RCTs where adults or parents of children were eligible for vaccination, the intervention was digital-push and the comparison group was non-digital. We included outcomes of vaccine uptake or series completion. We estimated summary effect sizes, heterogeneity using the χ2 test and quantified using the I2 statistic. Where heterogeneity remained significant, we conducted subgroup analyses. We assessed risk of bias, certainty of evidence and publication bias.
The search identified 159 peer-reviewed scientific publications. After review, a total of 12 manuscripts representing 13 empirical studies published between 2012 and 2016 were included. When comparing digital push interventions to non-digital ones, patients had 1.18[1.11,1.25] the odds of receiving vaccination or series completion compared to controls. In parents of children aged 18 and younger, those receiving digital push had a 1.22[1.15,1.30] increased odds compared to controls. Both analyses had high statistical heterogeneity, with I2 values of 86% and 79% respectively. The risk of bias was low with 10 of 13 studies considered low risk in five or more domains. The certainty of evidence for series completion was very low and for vaccine uptake was assessed to be moderate.
This study provides evidence that digital push technologies have a modest, positive impact on vaccine uptake and series completion compared to non-digital interventions.

The objective of this systematic review is to identify current computer-assisted technologies used for managing patients with a need to re-establish craniofacial appearance, subjective discomfort and stomatognathic function, and the extent of their clinical documentation. Electronic search strategies were used for locating clinical studies in MEDLINE through PubMed and in the Cochrane library, and in the grey literature through searches on Google Scholar. The searches for commercial digital products for use in oral rehabilitation resulted in identifying 225 products per November 2016, used for patient diagnostics, communication and therapy purposes, and for other computer-assisted applications in context with oral rehabilitation. About one-third of these products were described in about 350 papers reporting from clinical human studies. The great majority of digital products for use in oral rehabilitation has no clinical documentation at all, while the products from a distinct minority of manufacturers have frequently appeared in more or less scientific reports. Moore\'s law apply also to digital dentistry, which predicts that the capacity of microprocessors will continue to become faster and with lower cost per performance unit, and innovative software programs will harness these improvements in performance. The net effect is the noticeable short product life cycle of digital products developed for use in oral rehabilitation and often lack of supportive clinical documentation. Nonetheless, clinicians must request clinically meaningful information about new digital products to assess net benefits for the patients or the dental professionals and not accept only technological verbiage as a basis for product purchases.

OBJECTIVE: This paper reviews work over the past two years in Natural Language Processing (NLP) applied to clinical and consumer-generated texts.
METHODS: We included any application or methodological publication that leverages text to facilitate healthcare and address the health-related needs of consumers and populations.
RESULTS: Many important developments in clinical text processing, both foundational and task-oriented, were addressed in community- wide evaluations and discussed in corresponding special issues that are referenced in this review. These focused issues and in-depth reviews of several other active research areas, such as pharmacovigilance and summarization, allowed us to discuss in greater depth disease modeling and predictive analytics using clinical texts, and text analysis in social media for healthcare quality assessment, trends towards online interventions based on rapid analysis of health-related posts, and consumer health question answering, among other issues.
CONCLUSIONS: Our analysis shows that although clinical NLP continues to advance towards practical applications and more NLP methods are used in large-scale live health information applications, more needs to be done to make NLP use in clinical applications a routine widespread reality. Progress in clinical NLP is mirrored by developments in social media text analysis: the research is moving from capturing trends to addressing individual health-related posts, thus showing potential to become a tool for precision medicine and a valuable addition to the standard healthcare quality evaluation tools.

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

In the past decades, with the rapid development of high-throughput technologies, biology research has generated an unprecedented amount of data. In order to store and process such a great amount of data, cloud computing and MapReduce were applied to many fields of bioinformatics. In this paper, we first introduce the basic concepts of cloud computing and MapReduce, and their applications in bioinformatics. We then highlight some problems challenging the applications of cloud computing and MapReduce to bioinformatics. Finally, we give a brief guideline for using cloud computing in biology research.

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.

Ubiquitous (pervasive) computing is a term for a synergetic use of sensing, communication and computing. Pervasive use of computing has seen a rapid increase in the current decade. This development has propagated in applied sport science and everyday life. The work presents a survey of recent developments in sport and leisure with emphasis on technology and computational techniques. A detailed analysis on new technological developments is performed. Sensors for position and motion detection, and such for equipment and physiological monitoring are discussed. Aspects of novel trends in communication technologies and data processing are outlined. Computational advancements have started a new trend - development of smart and intelligent systems for a wide range of applications - from model-based posture recognition to context awareness algorithms for nutrition monitoring. Examples particular to coaching and training are discussed. Selected tools for monitoring rules\' compliance and automatic decision-making are outlined. Finally, applications in leisure and entertainment are presented, from systems supporting physical activity to systems providing motivation. It is concluded that the emphasis in future will shift from technologies to intelligent systems that allow for enhanced social interaction as efforts need to be made to improve user-friendliness and standardisation of measurement and transmission protocols.

BACKGROUND: Health care systems will integrate new computing paradigms in the coming years. Context-awareness computing is a research field which often refers to health care as an interesting and rich area of application.
OBJECTIVE: Through a survey of the research literature, we intended to derive an objective view of the actual dynamism of context awareness in health care, and to identify strengths and weaknesses in this field.
METHODS: After discussing definitions of context, we proposed a simple framework to analyse and characterize the use of context through three main axes. We then focused on context-awareness computing and reported on the main teams working in this area. We described some of the context-awareness projects in health care. A deeper analysis of the hospital-based projects demonstrated the gap between recommendations expressed for modelling context awareness and the actual use in a prototype. Finally, we identified pitfalls encountered in this area of research.
RESULTS: A number of opportunities remain for this evolving field of research. We found relatively few groups with such a specific focus. As yet there is no consensus as to the most appropriate models or attributes to include in context awareness. We conclude that a greater understanding of which aspects of context are important in a health care setting is required; the inherent sociotechnical nature of context-aware applications in health care; and the need to draw on a number of disciplines to conduct this research.

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.