Text mining

Given the past limitations on clinical practice training during the COVID-19 pandemic, a hybrid format program was developed, combining a time-lapse unfolding case study and high-fidelity simulation. This study assesses the effectiveness of a new form of clinical training from the perspective of student nurses. A questionnaire was administered to 159 second-year nursing students enrolled in the "Basic Nursing Practice II" course. Text mining was performed using quantitative text analysis for the following items: (1) aspects that were learned more deeply, (2) benefits, and (3) difficulties encountered with the new practice format...

BACKGROUND OBJECTIVES: Mosquito vectors are disease-causing insects, responsible for various life-threatening vector-borne diseases such as dengue, Zika, malaria, chikungunya, and lymphatic filariasis. In practice, synthetic insecticides are used to control the mosquito vector, but, the continuous usage of synthetic insecticides is toxic to human health resulting in communicable diseases. Non-toxic biocontrol agents such as bacteria, fungus, plants, and mosquito densoviruses play a vital role in controlling mosquitoes...

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions...

Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main obstacles to the task of biomedical named entity recognition. Deep learning methods require large amounts of training data, so the lack of data also affect the performance of model recognition...

BACKGROUND: Tremendous research efforts have been made in the Alzheimer's disease (AD) field to understand the disease etiology, progression and discover treatments for AD. Many mechanistic hypotheses, therapeutic targets and treatment strategies have been proposed in the last few decades. Reviewing previous work and staying current on this ever-growing body of AD publications is an essential yet difficult task for AD researchers. METHODS: In this study, we designed and implemented a natural language processing (NLP) pipeline to extract gene-specific neurodegenerative disease (ND) -focused information from the PubMed database...

Gut microbiota has been confirmed to be closely related to Alzheimer's disease (AD). Research on gut microbiota and AD has also increased significantly. This study aimed to conduct a bibliometric and visual analysis of published studies related to gut microbiota and AD. Based on the Web of Science Core Collection SCI-Expanded database, we utilize Excel 2019 and visualization analysis tools VOSviewer, Co-Occurrence13.2 (COOC13.2), Citespace, HistCite, and Bibliometrix (R-Tool of R-Studio) for analysis. A total of 1093 related kinds of literature were included, and the number of papers presented an overall increasing trend...

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature...

BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. METHODS: Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021...

BACKGROUNDS: With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers...

More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articles published about HTN in Eastern Mediterranean Region (EMRO) and artificial intelligence (AI) methods can analyze articles and extract top trends in each country. Present analysis uses Latent Dirichlet allocation (LDA) as an algorithm of topic modeling (TM) in text mining, to obtain subjective topic-word distribution from the 2790 studies over the EMRO. The period of checked studied is last 12 years and results of LDA analyses show that HTN researches published in EMRO discuss on changes in BP and the factors affecting it...

Bruton tyrosine kinases (BTKs) play critical roles in various diseases, including chronic lymphatic leukemia (CLL), Waldenström Macroglobulinemia, Marginal Zone Lymphoma, Mantle Cell Lymphoma (MCL), and Graft Versus Host diseases. BTKs are a family of tyrosine kinases involved in B lymphocyte signal transduction, development, and maturation. Their overexpression can lead to cancer as they are essential for the activation of the B Cell Receptor (BCR) signaling pathway. Blocking the activation of BTKs presents a promising approach for treating CLL...

Chemical-Disease relation (CDR) extraction aims to identify the semantic relations between chemical and disease entities in the unstructured biomedical document, which provides a basis for downstream tasks such as clinical medical diagnosis and drug discovery. Compared with general domain relation extraction, it needs a more effective representation of the whole document due to the specialized nature of texts in the biomedical domain, including the biomedical entity and entity-pair representation. In this paper, we propose a novel Multi-view Merge Representation (MMR) model to thoroughly capture entity and entity-pair representation of the document...

MOTIVATION: Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. RESULTS: In this study, we utilize features (genes) extracted using the text-mining of scientific literatures...

No abstract text is available yet for this article.

Natural language processing techniques enable extraction of valuable information from large amounts of published literature for the application of data science and technology, i.e. machine learning in the field of materials science. Nevertheless, the automated extraction of data from full-text documents remains a complex task. We propose a document-level natural language processing pipeline for literature extraction of comprehensive information on layered cathode materials for sodium-ion batteries. The pipeline enhances entity recognition with contextual supplementary information while capturing the article structure...

OBJECTIVE: Coronavirus infectious disease, that emerged in 2019 (COVID-19) has been a major public health issue not only in Japan, but also worldwide, and the implementation of a proper vaccination strategy has been important. To promote vaccination, the present study compared impressions of COVID-19 vaccinations stratified by the number of vaccinations among healthcare professional university students in Okayama, Japan, and suggests better vaccination strategies. METHOD: A total of 212 Japanese healthcare professional university students were enrolled in this clinical qualitative study using the text mining method...

BACKGROUND: Inguinal hernia in adults is a common and frequent disease in surgery, prone to occur in the elderly or in those with a weak abdominal wall. Despite its prevalence, Molecular mechanisms underlying inguinal hernia formation are unclear. OBJECTIVE: This study aims to identify potential gene markers for inguinal hernia and available drugs. METHODS: Pubmed2Ensembl text mining was used to identify genes related to "inguinal hernia" keywords...

No abstract text is available yet for this article.

Objective: Our study provides an innovative approach to exploring herbal formulas that contribute to the promotion of sustainability and biodiversity conservation. We employ data mining, integrating keyword extraction, association rules, and LSTM-based generative models to analyze classical Traditional Chinese Medicine (TCM) texts. We systematically decode classical Chinese medical literature, conduct statistical analyses, and link these historical texts with modern pharmacogenomic references to explore potential alternatives...

BACKGROUND: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens...