Identifying the Knowledge Structure and Trends of Outreach in Public Health Care: A Text Network Analysis and Topic Modeling.
Ontology highlight
ABSTRACT:
SUBMITTER: Park S
PROVIDER: S-EPMC8431096 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Couple and family researchers often collect open-ended linguistic data-either through free-response questionnaire items, or transcripts of interviews or therapy sessions. Because participants' responses are not forced into a set number of categories, text-based data can be very rich and revealing of psychological processes. At the same time, it is highly unstructured and challenging to analyze. Within family psychology, analyzing text data typically means applying a coding system, which can quantify text data but also has several limitations, including the time needed for coding, difficulties with interrater reliability, and defining a priori what should be coded. The current article presents an alternative method for analyzing text data called topic models (Steyvers & Griffiths, 2006), which has not yet been applied within couple and family psychology. Topic models have similarities to factor analysis and cluster analysis in that they identify underlying clusters of words with semantic similarities (i.e., the "topics"). In the present article, a nontechnical introduction to topic models is provided, highlighting how these models can be used for text exploration and indexing (e.g., quickly locating text passages that share semantic meaning) and how output from topic models can be used to predict behavioral codes or other types of outcomes. Throughout the article, a collection of transcripts from a large couple-therapy trial (Christensen et al., 2004) is used as example data to highlight potential applications. Practical resources for learning more about topic models and how to apply them are discussed.
| S-EPMC3468715 | biostudies-literature
Project description:Diatoms are fundamental carbon sources in a wide range of aquatic food webs and have the potential for wide application in addressing environmental change. Understanding the evolution of topics in diatom research will provide a clear and needed guide to strengthen research on diatoms. However, such an overview remains unavailable. In this study, we used Latent Dirichlet Allocation (LDA), a generative model, to identify topics and determine their trends (i.e., cold and hot topics) by analyzing the abstracts of 19,000 publications from the Web of Science that were related to diatoms during 1991-2018. A total of 116 topics were identified from a Bayesian model selection. The hot topics (diversity, environmental indicator, climate change, land use, and water quality) that were identified by LDA indicated that diatoms are increasingly used as indicators to assess water quality and identify modern climate change impacts due to intensive anthropogenic activities. In terms of cold topics (growth rate, culture growth, cell life history, copepod feeding, grazing by microzooplankton, zooplankton predation, and primary productivity) and hot topics (spatial-temporal distribution, morphology, molecular identification, gene expression, and review), we determined that basic studies on diatoms have decreased and that studies tend to be more comprehensive. This study notes that future directions in diatom research will be closely associated with the application of diatoms in environmental management and climate change to cope with environmental challenges, and more comprehensive issues related to diatoms should be considered.
| S-EPMC6722707 | biostudies-literature
Project description:Users of topic modeling methods often have knowledge about the composition of words that should have high or low probability in various topics. We incorporate such domain knowledge using a novel Dirichlet Forest prior in a Latent Dirichlet Allocation framework. The prior is a mixture of Dirichlet tree distributions with special structures. We present its construction, and inference via collapsed Gibbs sampling. Experiments on synthetic and real datasets demonstrate our model's ability to follow and generalize beyond user-specified domain knowledge.
| S-EPMC2943854 | biostudies-literature
Project description:In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.
| S-EPMC5244405 | biostudies-literature
Project description:This study uses YouTube content to explore trends in nursing start-ups. YouTube content can be used to understand the current trends regarding interest and awareness in various fields. The study was conducted in three stages: text mining, Delphi survey, and comparison. The frequency and degree centrality of keywords were analyzed in the text mining stage. In the Delphi survey, the 100 most frequent keywords were classified using a synthesis framework for nursing start-ups. In the comparison stage, the results of text mining and the Delphi survey were matched using a 2x2 matrix. Text mining identified "area," "business," "competence," "idea," and "success" as the most commonly used keywords. The keywords that showed the highest level of classification agreement in Delphi were "motivation," "advice," "obstacle," "business," "charisma," and "result." In the comparison using a 2x2 matrix, "dream," "idea," "opportunity," "leadership," "success," "benefit," and "satisfaction" emerged. The results indicate that interest in nursing start-ups develops at an early stage. In order to encourage nursing start-ups, it is necessary to strengthen business skills such as finance and budgeting, establish active policy support for such start-ups, and develop new nursing start-up items appropriate for the Fourth Industrial Revolution.
| S-EPMC7018134 | biostudies-literature
Project description:With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles' content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.
| S-EPMC10585361 | biostudies-literature
Project description:BackgroundDevelopmental Coordination Disorder (DCD) is a chronic neurodevelopmental condition that affects 5-6% of children. When not recognized and properly managed during the child's development, DCD can lead to academic failure, mental health problems and poor physical fitness. Physicians, working in collaboration with rehabilitation professionals, are in an excellent position to recognize and manage DCD. This study was designed to determine the feasibility and impact of an educational outreach and collaborative care model to improve chronic disease management of children with DCD.MethodsThe intervention included educational outreach and collaborative care for children with suspected DCD. Physicians were educated by and worked with rehabilitation professionals from February 2005 to April 2006. Mixed methods evaluation approach documented the process and impact of the intervention.ResultsPhysicians: 750 primary care physicians from one major urban area and outlying regions were invited to participate; 147 physicians enrolled in the project. Children: 125 children were identified and referred with suspected DCD. The main outcome was improvement in knowledge and perceived skill of physicians concerning their ability to screen, diagnose and manage DCD. At baseline 91.1% of physicians were unaware of the diagnosis of DCD, and only 1.6% could diagnose condition. Post-intervention, 91% of participating physicians reported greater knowledge about DCD and 29.2% were able to diagnose DCD compared to 0.5% of non-participating physicians. 100% of physicians who participated in collaborative care indicated they would continue to use the project materials and resources and 59.4% reported they would recommend or share the materials with medical colleagues. In addition, 17.6% of physicians not formally enrolled in the project reported an increase in knowledge of DCD.ConclusionPhysicians receiving educational outreach visits significantly improved their knowledge about DCD and their ability to identify and diagnose children with this condition. Physicians who collaborated with occupational therapists in providing care reported more confidence in diagnosing children with DCD and were more likely to continue to use screening measures and to provide educational materials to families.
| S-EPMC2254381 | biostudies-literature
Project description:BackgroundHospice care, a type of end-of-life care provided for dying patients and their families, has been rooted in China since the 1980s. It can improve receivers' quality of life as well as ease their economic burden. The Chinese mass media have continued to actively dispel misconceptions surrounding hospice care and deliver the latest information to citizens.ObjectiveThis study aims to retrieve and analyze news reports on hospice care in order to gain insight into whether any differences existed in heath information delivered over time and to evaluate the role of mass media in health communication in recent years.MethodsWe searched the Huike (WiseSearch) news database for relevant news reports from Chinese mass media released between 2014 and 2019. We defined two time periods for this study: (1) January 1, 2014, to December 31, 2016, and (2) January 1, 2017, to December 31, 2019. The data cleaning process was completed using Python. We determined appropriate topic numbers for these two periods based on the coherence score and applied latent Dirichlet allocation topic modeling. Keywords for each topic and corresponding topics' names were then generated. The topics were plotted into different circles, and their distances on the 2D plane was represented by multidimensional scaling.ResultsAfter removing duplicated and irrelevant news articles, we obtained a total of 2227 articles. We chose 8 as the suitable topic number for both study periods and generated topic names and associated keywords. The top 3 most reported topics in the first period were patient treatment, hospice care stories, and development of health care services and health insurance, accounting for 18.68% (178/953), 16.58% (158/953), and 14.17% (135/953) of the collected reports, respectively. The top 3 most reported topics in the second period were hospice care stories, patient treatment, and development of health care services, accounting for 15.62% (199/953), 15.38% (15.38/953), and 14.27% (182/953), respectively.ConclusionsTopic modeling of news reports gives us a better understanding of the patterns of health communication about hospice care by mass media. Chinese mass media frequently reported on hospice care in April of every year on account of a traditional Chinese festival. Moreover, an increase in coverage was observed in the second period. The two periods shared 6 similar topics, of which patient treatment outstrips hospice care stories was the most reported topic in the second period, implying the humanistic spirit behind the reports. Based on the findings of this study, we suggest stakeholders cooperate with the mass media when planning to update policies.
| S-EPMC8569548 | biostudies-literature
Project description:BackgroundGiven the significant impact on public health and drug development, drug safety has been a focal point and research emphasis across multiple disciplines in addition to scientific investigation, including consumer advocates, drug developers and regulators. Such a concern and effort has led numerous databases with drug safety information available in the public domain and the majority of them contain substantial textual data. Text mining offers an opportunity to leverage the hidden knowledge within these textual data for the enhanced understanding of drug safety and thus improving public health.MethodsIn this proof-of-concept study, topic modeling, an unsupervised text mining approach, was performed on the LiverTox database developed by National Institutes of Health (NIH). The LiverTox structured one document per drug that contains multiple sections summarizing clinical information on drug-induced liver injury (DILI). We hypothesized that these documents might contain specific textual patterns that could be used to address key DILI issues. We placed the study on drug-induced acute liver failure (ALF) which was a severe form of DILI with limited treatment options.ResultsAfter topic modeling of the "Hepatotoxicity" sections of the LiverTox across 478 drug documents, we identified a hidden topic relevant to Hy's law that was a widely-accepted rule incriminating drugs with high risk of causing ALF in humans. Using this topic, a total of 127 drugs were further implicated, 77 of which had clear ALF relevant terms in the "Outcome and management" sections of the LiverTox. For the rest of 50 drugs, evidence supporting risk of ALF was found for 42 drugs from other public databases.ConclusionIn this case study, the knowledge buried in the textual data was extracted for identification of drugs with potential of causing ALF by applying topic modeling to the LiverTox database. The knowledge further guided identification of drugs with the similar potential and most of them could be verified and confirmed. This study highlights the utility of topic modeling to leverage information within textual drug safety databases, which provides new opportunities in the big data era to assess drug safety.
| S-EPMC4304199 | biostudies-literature
Project description:Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text.
| S-EPMC7432829 | biostudies-literature