Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial ...expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research.
Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a ...bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field.
With the basis of 4361 publications from both Web of Science and PubMed during the year 2008-2017, the analysis utilizes methods including topic modelling and science mapping analysis.
Utilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected.
This systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Transfer learning techniques have been broadly applied in applications where labeled data in a target domain are difficult to obtain while a lot of labeled data are available in related source ...domains. In practice, there can be multiple source domains that are related to the target domain, and how to combine them is still an open problem. In this paper, we seek to leverage labeled data from multiple source domains to enhance classification performance in a target domain where the target data are received in an online fashion. This problem is known as the online transfer learning problem. To achieve this, we propose novel online transfer learning paradigms in which the source and target domains are leveraged adaptively. We consider two different problem settings: homogeneous transfer learning and heterogeneous transfer learning. The proposed methods work in an online manner, where the weights of the source domains are adjusted dynamically. We provide the mistake bounds of the proposed methods and perform comprehensive experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms.
There are various ways to incorporate syntax knowledge into neural machine translation (NMT). However, quantifying the dependency syntactic intimacy (DSI) between word pairs in a dependency tree has ...not being considered to use in attentional and transformer-based NMT. In this paper, we innovatively propose a variant of Tree-LSTM to capture the syntactic dependency degree (SDD) between word pairs in dependency trees. Two syntax-aware distances, including a tuned syntax distance and a
ρ
-dependent distance, are proposed. For attentional NMT, two syntax-aware attentions based on two syntax-aware distances are proposed for attentional NMT, and we also design a dual attention to simultaneously generate global context and dependency syntactic context. For transformer-based NMT, we explicitly incorporate the dependency syntax into self-attention network (SAN) to propose a syntax-aware SAN. Experiments on IWSLT’17 English–German, IWSLT Chinese–English and WMT’15 English–Finnish translation tasks show that our syntax-aware NMT significantly improves translation quality by comparing with baseline methods, even the state-of-the-art transformer-based NMT.
Question answering, serving as one of important tasks in natural language processing, enables machines to understand questions in natural language and answer the questions concisely. From web search ...to expert systems, question answering systems are widely applied to various domains in assisting information seeking. Deep learning methods have boosted various tasks of question answering and have demonstrated dramatic effects in performance improvement for essential steps of question answering. Thus, leveraging deep learning methods for question answering has drawn much attention from both academia and industry in recent years. This paper provides a systematic review of the recent development of deep learning methods for question answering. The survey covers the scope including methods, datasets, and applications. The methods are discussed in terms of network structure characteristics, methodology innovations, and their effectiveness. The survey is expected to be a contribution to the summarization of recent research progress and future directions of deep learning methods for question answering.
Natural language processing (NLP) has become an increasingly significant role in advancing medicine. Rich research achievements of NLP methods and applications for medical information processing are ...available. It is of great significance to conduct a deep analysis to understand the recent development of NLP-empowered medical research field. However, limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the academic output of NLP in medical research field.
We conducted a bibliometric analysis on NLP-empowered medical research publications retrieved from PubMed in the period 2007-2016. The analysis focused on three aspects. Firstly, the literature distribution characteristics were obtained with a statistics analysis method. Secondly, a network analysis method was used to reveal scientific collaboration relations. Finally, thematic discovery and evolution was reflected using an affinity propagation clustering method.
There were 1405 NLP-empowered medical research publications published during the 10 years with an average annual growth rate of 18.39%. 10 most productive publication sources together contributed more than 50% of the total publications. The USA had the highest number of publications. A moderately significant correlation between country's publications and GDP per capita was revealed. Denny, Joshua C was the most productive author. Mayo Clinic was the most productive affiliation. The annual co-affiliation and co-country rates reached 64.04% and 15.79% in 2016, respectively. 10 main great thematic areas were identified including Computational biology, Terminology mining, Information extraction, Text classification, Social medium as data source, Information retrieval, etc. CONCLUSIONS: A bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented. The results can assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.
Celotno besedilo
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical ...data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Targeted at analyzing the research status and trends of the educational technology field, this study conducted a bibliometric analysis on research topics, author profiles, and collaboration networks ...using a top-ranked journal
Computers & Education
(ISSN: 0360-1315). Using the Web of Sciences database, we retrieved 3963 articles published by the journal during the period 1978–2018. The annual distribution of articles demonstrates a significant increase in the journal publications, especially from 2005 to 2011. The scientific collaboration between authors, institutions, and countries/regions has become increasingly close. The scientific collaboration rate between authors from the same institution, and from the same country/region, is relatively higher compared with those from different institutions and countries/regions. Keyword evolution analysis highlights some prevalent topics such as “interactive learning environment,” “teaching/learning strategies,” “pedagogical issue,” and “improving classroom teaching.” Findings of this study provide a comprehensive overview of the articles on educational technology over the past 40 years.
Derived from knowledge bases, knowledge graphs represent knowledge expressions in graphs, which utilize nodes and edges to denote entities and relations conceptually. Knowledge graph can be described ...in textual triple form, consisting of head entities, tail entities and relations between entities. In order to represent elements in knowledge graphs, knowledge graph embedding techniques are proposed to map entities and relations into continuous vector spaces as numeric vectors for computational efficiency. Convolution-based knowledge graph embedding models have promising performance for knowledge graph representation learning. However, the input of those neural network-based models is frequently in handmade forms and may suffer from low efficiency in feature extraction procedure of the models. In this paper, a convolutional autoencoder is proposed for knowledge graph representation learning with entity pairs as input, aiming to obtain corresponding hidden relation representation. In addition, a bi-directional relation encoding network is utilized to represent semantic of entities in different directional relation patterns, as an encoder to output representation for initialization of the convolutional autoencoder. Experiments are conducted on standard datasets including, WN18RR, Kinship, NELL-995 and FB15k-237 as a link prediction task. Besides, input embedding matrix composed of different ingredients is designed to evaluate performances of the convolutional autoencoder. The results demonstrate that our model is effective in learning representation from entity feature interactions.
At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration network, which constructed from ...real-world migration records and the Susceptible–Exposed–Infected–Removed model, is utilized to predict the epidemic spreading of the COVID-19 in more than 300 cities in China. However, the model has more than 1800 unknown parameters, which is a challenging task to estimate all unknown parameters from historical data within a reasonable computation time. In this article, we proposed a pseudocoevolutionary simulated annealing (SA) algorithm for identifying these unknown parameters. The large volume of unknown parameters of this model is optimized through three procedures co-adapted SA-based optimization processes, respectively. Our results confirm that the proposed method is both efficient and robust. Then, we use the identified model to predict the trends of the epidemic spreading of the COVID-19 in these cities. We find that the number of infections in most cities in China has reached their peak from February 29, 2020, to March 15, 2020. For most cities outside Hubei province, the total number of infected individuals would be less than 100, while for most cities in Hubei province (exclude Wuhan), the total number of infected individuals would be less than 3000.
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