Mapping Topics and Topic Bursts in PNAS

Scientific research is highly dynamic. New areas of science continually evolve;others gain or lose importance, merge or split. Due to the steady increase in the number of scientific publications it is hard to keep an overview of the structure and dynamic development of one's own field of science, much less all scientific domains. However, knowledge of hot topics, emergent research frontiers, or change of focus in certain areas is a critical component of resource allocation decisions in research labs, governmental institutions, and corporations. This paper demonstrates the utilization of Kleinberg's burst detection algorithm, co-word occurrence analysis, and graph layout techniques to generate maps that support the identification of major research topics and trends. The approach was applied to analyze and map the complete set of papers published in the Proceedings of the National Academy of Sciences (PNAS) in the years 1982-2001. Six domain experts examined and commented on the resulting maps in an attempt to reconstruct the evolution of major research areas covered by PNAS

Application of Social Network Analysis to Health Care Sectors

Objectives: This study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine. Methods: Papers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals. Results: The core keyword with the highest centrality value was “Risk Factor, ” followed b

Impact of general practice / family medicine clerkships on Japanese medical students: Using text mining to analyze reflective writing

Background: In order for general practice / family medicine clerkships to be improved in undergraduate medical education, it is necessary to clarify the impacts of general practice / family medicine clerkships. Using text mining to analyze the reflective writing of medical students may be useful for further understanding the impacts of clinical clerkships on medical students. Methods: The study involved 125 fifth-year Fukushima Medical University School of Medicine students in the academic year 2018-2019. The settings were three clinics and the study period was 5 days. The clerkships included outpatient and home visits. Students' reflective writing on their clerkship experience was collected on the final day. Text mining was used to extract the most frequent words (nouns) from the reflective writing. A co-occurrence network map was created to illustrate the relationships between the most frequent words. Results: 124 students participated in the study. The total number of sentences extracted was 321 and the total number of words was 10,627. The top five frequently-occurring words were patient, home-visit, medical practice, medical care, and family. From the co-occurrence network map, a co-occurrence relationship was recognized between home-visit and family. Conclusion: Data suggest that medical students may learn the necessity of care for the family as well as the patient in a home-care setting

Parallel Processing of Burst Detection in Large-Scale Document Streams and Its Performance Evaluation

Online documents on the Internet are represented asa document stream because the documents have a temporal order.This has resulted in numerous studies on extracting a frequentphenomenon (involving keywords, users, locations etc.) knownas a burst. Recently, with the growth of interest in social media,the number of documents created on the Internet has increasedexponentially. Therefore, the speed-up of burst detection ina large-scale document stream is one of the most importantchallenges. In this paper, we propose a novel parallelizationmethod for the parallel processing of Kleinberg’s burst detectionalgorithm in a large-scale document stream. Specifically, wepresent a technique to combine the inter-task parallelizationmodel with the intra-task parallelization model. This combinationcan achieve seamless dynamic load balancing and detect burstsin a large-scale document streams in memory

Text Data Mining: Theory and Methods

This paper provides the reader with a very brief introduction to some of the theory and methods of text data mining. The intent of this article is to introduce the reader to some of the current methodologies that are employed within this discipline area while at the same time making the reader aware of some of the interesting challenges that remain to be solved within the area. Finally, the articles serves as a very rudimentary tutorial on some of techniques while also providing the reader with a list of references for additional study.Comment: Published in at http://dx.doi.org/10.1214/07-SS016 the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.imstat.org

Water research paradigm shifts in South Africa

We performed a scientometric analysis of water research publications extracted from four decades of South African related papers to identify paradigms and paradigm shifts within water research in South Africa. Between 1977 and 1991, research publications are dominated by research into technical and engineering solutions, as well as designs and plans to secure water supply. From 1992 to 2001, publications on water pollution, water quality, water resource management and planning are prominent. The second major paradigm is observed from 2001 to 2011 in which the emphasis is on planning, modelling, catchment-scale studies and a multidisciplinary approach to research. Another transition period, towards the end of 2011, is characterised by uncertainty, although it also shows the prominence of key concepts such as participation, governance and politics in water management. The second aim of this study was to identify and prioritise current and future water research questions through the participation of a wide range of researchers from across the country, and to relate these questions to research paradigms, issues and concerns in water in South Africa. Over 1600 questions were collected, reduced in number and then prioritised by specialists in the water sector. The majority (78%) of questions offered by respondents in the South African case study dealt with relatively short- to medium-term research requirements with 47% of questions focused on medium-term issues such as supplying water, service delivery and technical solutions