Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques.

Biomedical natural language processing (NLP) has an important role in extracting consequential information in medical discharge notes. Detecting meaningful features from unstructured notes is a challenging task in medical document classification. The domain specific phrases and different synonyms within the medical documents make it hard to analyze them. Analyzing clinical notes becomes more challenging for short documents like abstract texts. All of these can result in poor classification performance, especially when there is a shortage of the clinical data in real life. Two new approaches (an ontology-guided approach and a combined ontology-based with dictionary-based approach) are suggested for augmenting medical data to enrich training data. Three different deep learning approaches are used to evaluate the classification performance of the proposed methods. The obtained results show that the proposed methods improved the classification accuracy in clinical notes classification

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Do tablets cure the pedagogy headache?

Tablet devices have made a huge impact in schools and in 2015 they were predicted to outsell personal computers (Gartner, 2014). 70 per cent of UK schools are estimated to be using tablets (BBC, online) and across Europe, “laptops, tablets and net-books are becoming pervasive” (EU schoolnet, 2014). As these devices become established in schools they both support and develop existing practice (Burden, Hopkins, Male, Martin and Trala, 2012; Baran, 2014), but are also starting to challenge some existing models of thinking and pedagogy (Fullan and Langworth, 2014; Kearney, Schuck, Burden and Aubusson, 2012) and also teachers’ attitudes towards learning and teaching (Ertmer, 1999; Burden and Hopkins, 2015). In offering opportunities for learning to become more authentic, personal and collaborative (Kearney et al., 2012) there are opportunities for teachers to start to redesign the ways in which learning is taking place (Puentedura, 2010; McCormick and Scrimshaw, 2001). Traxler defines mobile learning as “an educational process, in which handheld devices or palmtops are the only or dominant used technology tools” (2007: 2) and Kearney et al. (2012) argue that it has the potential to revolutionise the learning process in allowing individuals to determine their own independent paradigms and frameworks of learning. These devices are also sophisticated producers of digital artefacts and children and teachers are capable of being co-producers of learning materials

Towards a service-oriented architecture for a mobile assistive system with real-time environmental sensing

Dalian Key Lab. of Smart Medical and Healthcare, Computer Science Department, Dalian University, China,With the growing aging population, age-related diseases have increased considerably over the years. In response to these, ambient assistive living (AAL) systems are being developed and are continually evolving to enrich and support independent living. While most researchers investigate robust activity recognition (AR) techniques, this paper focuses on some of the architectural challenges of the AAL systems. This work proposes a system architecture that fuses varying software design patterns and integrates readily available hardware devices to create wireless sensor networks for real-time applications. The system architecture brings together the service-oriented architecture (SOA), semantic web technologies, and other methods to address some of the shortcomings of the preceding system implementations using off-the-shelf and open source components. In order to validate the proposed architecture, a prototype is developed and tested positively to recognize basic user activities in real time. The system provides a base that can be further extended in many areas of AAL systems, including composite AR

Verb Semantics for English-Chinese Translation

A common practice in operational Machine Translation (MT) and Natural Language Processing (NLP) systems is to assume that a verb has a fixed number of senses and rely on a precompiled lexicon to achieve large coverage. This paper demonstrates that this assumption is too weak to cope with the similar problems of lexical divergences between languages and unexpected uses of words that give rise to cases outside of the precompiled lexicon coverage. We first examine the lexical divergences between English verbs and Chinese verbs. We then focus on a specic lexical selection problem - translating English change-of-state verbs into Chinese verb compounds. We show that an accurate translation depends not only on information about the participants, but also on contextual information. Therefore, selectional restrictions on verb arguments lack the necessary power for accurate lexical selection. Second, we examine verb representation theories and practices in MT systems and show that under the fixed sense assumption, the existing representation schemes are not adequate for handling these lexical divergences and extending existing verb senses to unexpected usages. We then propose a method of verb representation based on conceptual lattices which allows the similarities among different verbs in different languages to be quantitatively measured. A prototype system UNICON implements this theory and performs more accurate MT lexical selection for our chosen set of verbs. An additional lexical module for UNICON is also provided that handles sense extension