Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.

Background: Electronic medical records store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in clinical decision support systems is substantial, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for natural language processing in clinical decision support systems. Efficient abbreviation disambiguation methods are needed for effective information extraction. Objective: This study aims to enhance the one-to-all (OTA) framework for clinical abbreviation expansion, which uses a single model to predict multiple abbreviation meanings. The objective is to improve OTA by developing context-candidate pairs and optimizing word embeddings in Bidirectional Encoder Representations From Transformers (BERT), evaluating the model's efficacy in expanding clinical abbreviations using real data. Methods: Three datasets were used: Medical Subject Headings Word Sense Disambiguation, University of Minnesota, and Chia-Yi Christian Hospital from Ditmanson Medical Foundation Chia-Yi Christian Hospital. Texts containing polysemous abbreviations were preprocessed and formatted for BERT. The study involved fine-tuning pretrained models, ClinicalBERT and BlueBERT, generating dataset pairs for training and testing based on Huang et al's method. Results: BlueBERT achieved macro- and microaccuracies of 95.41% and 95.16%, respectively, on the Medical Subject Headings Word Sense Disambiguation dataset. It improved macroaccuracy by 0.54%-1.53% compared to two baselines, long short-term memory and deepBioWSD with random embedding. On the University of Minnesota dataset, BlueBERT recorded macro- and microaccuracies of 98.40% and 98.22%, respectively. Against the baselines of Word2Vec + support vector machine and BioWordVec + support vector machine, BlueBERT demonstrated a macroaccuracy improvement of 2.61%-4.13%. Conclusions: This research preliminarily validated the effectiveness of the OTA method for abbreviation disambiguation in medical texts, demonstrating the potential to enhance both clinical staff efficiency and research effectiveness.

Menstrual health under the scientific microscope: Text mining analysis.

Menstrual health represents an interdisciplinary concern that necessitates a broad, integrated understanding beyond its biological foundations, encompassing social, psychological, and cultural dimensions. This study examines whether the corpus of scientific literature from 1970 to 2023 aligns with this holistic perspective by exploring the evolving paradigms within menstrual health. Grounded in Kuhn's theoretical framework, the research delves into thematic shifts, author collaborations, and international partnerships that have emerged over the decades. Utilizing advanced text-mining methodologies, we analyzed a dataset of 34,854 documents obtained from Institute for Scientific Information Web of Science and PubMed in September 2023. These documents were processed through deduplication and data cleaning to ensure accuracy. The study employs a combination of univariate analyses, correspondence factor analyses, hierarchical cluster analyses, and network analyses to uncover insights into thematic evolution and collaborative dynamics within menstrual health research. Thematic analysis reveals three distinct periods in menstrual health research, depicting evolving paradigms. In the first period (1970-1996), the focus was on reproductive health, infertility treatments, hormonal regulation, and epidemiology. The second period (1997-2017) witnesses a transition, emphasizing menstrual health and social inequalities, gynecological studies, and contraception. The third period (2018-2023) maintains a focus on contraception and reproductive health but introduces a pronounced psychological dimension, emphasizing menstrual disorders, gynecological surgery, and socioeconomic concerns. Collaboration analysis indicates increased connectivity, consolidation of researcher communities, and a shift toward interdisciplinary approaches. While international collaborations demonstrate global commitment, geographical concentration prompts questions about paradigm universality. The study shows the existence and evolution of the menstrual health paradigm. Findings suggest a trajectory toward paradigmatic inscription, marked by heightened collaboration and global commitment. Acknowledging the pivotal role of biological aspects, the study underscores the need for a balanced, holistic understanding of menstrual health. Continued efforts are essential to tailor interventions, foster inclusivity, and honor diverse cultural and psychological realities related to menstruation.

Menstrual health isn't just about biology­it also touches on social, emotional, and cultural aspects. But how have researchers addressed this complex topic over the years?What did the researchers do?In this study, the researchers examined research on menstrual health from 1970 to 2023. They used special tools to analyze how this research has changed over time, what themes were explored, and how scientists around the world have worked together.Why does this matter?The researchers wanted to see if there's a clear and consistent way that menstrual health is approached in research. If there is, it can help improve health policies and medical practices. But if not, it could mean that different approaches are scattered, leading to less effective solutions.What did the researchers find?After analyzing over 34,000 documents, the researchers identified three main phases in menstrual health research:• From 1970 to 1996, the focus was mostly on reproductive health.• Between 1997 and 2017, researchers started to look more at social inequalities and contraception.• Since 2018, there has been a new focus on the psychological aspects of menstruation.They also noticed that researchers are collaborating more than before, which is a good sign for a more integrated approach to menstrual health. However, even though interest in this topic is global, certain regions are more active in this research than others. This raises the question: is there a shared vision worldwide?Why is this important?This work highlights how menstrual health research has evolved and suggests that a more comprehensive approach is needed to guide future studies and health policies. By better understanding the different dimensions of menstrual health, policymakers, healthcare providers, and researchers can create more inclusive and effective solutions for women everywhere.

Biomacromolecules-based nanoparticle formulations for the treatment of osteoporosis: A bibliometric analysis.

This bibliometric analysis examined biomacromolecule-based nanoparticle formulations, emphasizing polysaccharides, for osteoporosis treatments from 2009 to 2024. Using the Web of Science database, we tracked around 141 publications, of which 117 were original research articles. This shows an emerging trend in biomacromolecule-based nanoparticle formulations based on the total number of publications. On further analysis, we found 61 original articles that focused on polysaccharides-based nanoparticles for drug delivery. This study also identified 'pharmacology and pharmacy,' 'materials science, biomaterials, and 'nanoscience and nanotechnology' as the primary research areas, emphasizing the field's interdisciplinary nature. The 'Journal of Drug Delivery Science and Technology' emerged as a significant journal for this research theme. Notable contributions came from the Egyptian Knowledge Bank and funding organizations like the National Natural Science Foundation of China. China, India, and Egypt are the top three research-productive countries in this field. This novel study underscores a dynamic, globally collaborative effort to advance polysaccharide-based nanoparticle applications in osteoporosis treatment. Based on the current publications, it also highlights challenges and future perspectives in the field.

Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques.

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption.

Trends and Gaps in the Scientific Literature about the Effects of Nutritional Supplements on Canine Leishmaniosis.

In canine leishmaniosis (CanL), complex interactions between the parasites and the immunological background of the host influence the clinical presentation and evolution of infection and disease. Therefore, the potential use of nutraceuticals as immunomodulatory agents becomes of considerable interest. Some biological principles, mainly derived from plants and referred to as plant-derived nutraceuticals, are considered as supplementation for Leishmania spp. infection. This study provides a systematic review regarding the use of nutraceuticals as a treatment using a text mining (TM) and topic analysis (TA) approach to identify dominant topics of nutritional supplements in leishmaniosis-based research, summarize the temporal trend in topics, interpret the evolution within the last century and highlight any possible research gaps. Scopus® database was screened to select 18 records. Findings revealed an increasing trend in research records since 1994. TM identified terms with the highest weighted frequency and TA highlighted the main research areas, namely "Nutraceutical supports and their anti-inflammatory/antioxidant properties", "AHCC and nucleotides in CanL", "Vit. D3 and Leishmaniosis", "Functional food effects and Leishmaniosis" and "Extract effects and Leishmaniosis". Despite the existing academic interest, there are only a few studies on this issue so far, which reveals a gap in the literature that should be filled.

Text Mining and Topic Analysis for Ostriches' Welfare Based on Systematic Literature Review from 1983 to 2023.

Ostriches can be utilized as multipurpose animals suitable for producing meat, eggs, feathers, and leather. This growing interest in ostrich farming leads to an increased demand for comprehensive information on their management. But, little attention is paid to the consequences for their welfare. The study aimed to perform a research literature analysis on ostriches' welfare using the text mining (TM) and topic analysis (TA) methods. It identifies prevailing topics, summarizes their temporal trend within the last forty years, and highlights potential research gaps. According to PRISMA guidelines, a literature exploration was achieved using the Scopus® database, retaining keywords about ostriches' welfare. Papers distributed in the English language from 1983 to 2023 were included. Descriptive statistics, TM, and TA were applied to a total of n. 122 documents included. The findings revealed an increasing trend in research records since 1994. TM recognized the terms with the highest weighted frequency and TA identified the main topics of the research area, in the following order: "health and management", "feeding and nutrition", "welfare reproduction", "egg production", and "welfare during transport". The study confirms the increased focus on ostriches' welfare but shows that further studies are required to ensure the welfare of this species.

Utilising Text Mining to Determine Japanese Dental Hygiene Students' Perceptions of Dental Hygienists and Dentists.

OBJECTIVES: This longitudinal text-mining study examines dental hygiene students' perceptions of dental hygienists and dentists at three different points in time during their training course. The null hypothesis of the study was that there would be no change in the dental hygiene students' perceptions of the dental hygienists and dentists over the course of 3 years. MATERIALS AND METHODS: First-year dental hygiene students participated in this study beginning with the academic year 2020. The questionnaires were conducted in 2020, 2021, and 2022. Participants were asked to write their perceptions of dental hygienists and dentists on the questionnaire sheets, and a quantitative text-mining analysis was performed. RESULTS: Initially, 59 female students were assessed for enrollment in this study, and the overall participation rate was 88.1%. The first-year students' perceptions of dental hygienists were "assist" and "beside" the dentist based on the co-occurrence group, while in the second-year, "cleaning" and "tooth" emerged in this group, and a new group of "cordinal-listen-story" materialised. In the third year, these groups merged into one group centered on "kind." In the perceptions of dentists, the word "fear" was the most frequent before clinical training, while the frequency of the world "kind" increased after clinical training. The word "treat" was ranked third in the second year of curriculum and then first in the final year. CONCLUSIONS: The null hypothesis was rejected. It can be concluded that if interactions between dental hygiene and dental students are systematically incorporated from early undergraduate education, clinical practice will be more meaningful and lead to stronger intraprofessional collaboration in future clinical practice.

How do policies promote the sustainable development of older-adult care industry? A configuration analysis based on policy tools.

Background: Under the background of population aging in China, the demand for older-adult care services and products is growing, and the older-adult care industry has great development prospects. A sound older-adult care policy system, that is, an effective policy tool mix, plays an important role in improving the sustainable development of older-adult care industry. Materials and methods: Based on older-adult care policy documents from 31 Chinese provinces, this research extracts older-adult care policy tools via text mining. Then extracted policy tools are taken as conditional variables, and the development of older-adult care industry, which is manifested by the number of older-adult care companies across 31 regions is taken as the result variable. Through applying qualitative comparative analysis, the combined effect of different policy tools on the development of older-adult care industry is obtained. Results and discussions: Results show that a single policy tool cannot constitute the necessary condition to facilitate the older-adult care industry. Hence, policy tools should be applied in combination. Five sustainable policy tool mixes which can promote the development of older-adult care industry are summarized, namely supporting policy-driven mode, fiscal and tax support mode led by supply-oriented policy tools, double-team mode driven by fiscal and tax support and the consumer market, multi-subject joint force mode, and technology compensation mode. The overall findings of this study imply that exploring the policy tool combinations is of vital importance to the sustainable development of older-adult care industry.

Predicting the sample size of randomized controlled trials using natural language processing.

Objectives: Extracting the sample size from randomized controlled trials (RCTs) remains a challenge to developing better search functionalities or automating systematic reviews. Most current approaches rely on the sample size being explicitly mentioned in the abstract. The objective of this study was, therefore, to develop and validate additional approaches. Materials and Methods: 847 RCTs from high-impact medical journals were tagged with 6 different entities that could indicate the sample size. A named entity recognition (NER) model was trained to extract the entities and then deployed on a test set of 150 RCTs. The entities' performance in predicting the actual number of trial participants who were randomized was assessed and possible combinations of the entities were evaluated to create predictive models. The test set was also used to evaluate the performance of GPT-4o on the same task. Results: The most accurate model could make predictions for 64.7% of trials in the test set, and the resulting predictions were equal to the ground truth in 93.8%. GPT-4o was able to make a prediction on 94.7% of trials and the resulting predictions were equal to the ground truth in 90.8%. Discussion: This study presents an NER model that can extract different entities that can be used to predict the sample size from the abstract of an RCT. The entities can be combined in different ways to obtain models with different characteristics. Conclusion: Training an NER model to predict the sample size from RCTs is feasible. Large language models can deliver similar performance without the need for prior training on the task although at a higher cost due to proprietary technology and/or required computational power.

Integrating text mining with network models for successful target identification: in vitro validation in MASH-induced liver fibrosis.

An in silico target discovery pipeline was developed by including a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. This approach integrates text mining, network biology, and artificial intelligence/machine learning with clinical transcriptome data for optimal translational power. At the mechanistic level, the critical components influencing disease progression were identified from the disease network using in silico knockouts. The top-ranked genes were then subjected to a target efficacy analysis, following which the top-5 candidate targets were validated in vitro. Three targets, including EP300, were confirmed for their roles in liver fibrosis. EP300 gene-silencing was found to significantly reduce collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 showed significant inhibition of collagen to the extent of 81% compared to the TGFß-stimulated control (1 µM inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human-disease-mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, while the inclusion of clinical data boosts its translational power and ensures identification of the most relevant disease pathways. In silico knockouts thus provide crucial molecular insights for successful target identification.

Public Perceptions of Enuresis: Insights From Online Communities in South Korea and the United States.

PURPOSE: To establish a foundation for raising awareness and disseminating accurate information about enuresis-one of the most challenging conditions to discuss openly-this paper examines public perceptions of enuresis. METHODS: This paper collected title and text data from posts related to enuresis on the top popular online platforms such Naver Cafe in South Korea and Reddit in the United States (US). The data along with the thematic subcommunities where the posts were uploaded, was analyzed and visualized using word cloud, Latent Dirichlet Allocation (LDA) topic modeling, and pyLDAvis. RESULTS: The findings reveal both similarities and differences in how the patients from the 2 countries addressed enuresis online. In both countries, enuresis symptoms were a daily concern, and individuals used online platforms as a space to talk about their experiences. However, South Koreans were more inclined to describe symptoms within region-based communities or mothers' forums, where they exchanged information and shared experiences before consulting a doctor. In contrast, US patients with medical experience or knowledge frequently discussed treatment processes, lifestyle adjustments, and medication options. CONCLUSION: South Koreans tend to be cautious when selecting and visiting hospitals, often relying on others for advice and preparation before seeking medical attention. Compared to online communities in the US, Korean users are more likely to seek preliminary diagnoses based on nonprofessional opinions. Consequently, it is important to lower the barriers for patients to access professional medical advice to mitigate the potential harm of relying on nonprofessional opinions. Additionally, there is a need to raise awareness so that adults can recognize and address their symptoms in a timely manner.

Text mining approach in chicken meat welfare.

The aim of the study was to analyze the aspects affecting broiler welfare with the use of Text Mining technique. This approach converts text into numerical data and analyzes word frequency distributions, enabling the extraction of useful information and the identification of relationships between elements. Text mining has limitations, i.e. ambiguity and context sensitivity, making it difficult to capture nuanced meanings. The search was carried out with Scopus using the term "Welfare" with the keywords "Chicken", "Broiler", "Broiler chicken", and "Chicken meat", to create a database of abstracts. Text Mining and Topic Analysis were performed on the abstracts (1228 documents) using the Software R 4.3.1., analyzing also the weight of bigram and trigram. Publications on broiler welfare are present in the bibliography since 1990's, but in the last 10 years, for the interest of public opinion, the numbers of publications significantly increased (76.5% of all documents published). USA, Brazil, and Europe-27 published 60% of the documents found. The works were published in a high number of journals, but 37% of them are published in only 4 journals (Poultry Science, Animals, Applied Animal Behavior Science and Animal Welfare). Text Mining analysis identified key terms related to the slaughter phase, housing management, and environmental conditions such as light quality and quantity. Moreover, a high correlation was found between some terms, underlying the importance of the effects of rearing, slaughter phases and litter management on broiler welfare. Most of the countries focused their research on some specific topics identified by Topic Analysis, mainly genetic selection, feeding, stocking density, slaughter, and consumer perceptions. Poultry Science published the highest number of papers (18%) and the topics more investigated were growing performance, transport and slaughter, and litter management. In conclusion, the high number of publications on chicken welfare underlines the importance of broiler welfare both in Europe and in other countries, even if it is difficult to identify common research topics among the geographic areas and the evolution over the time.

A comprehensive evaluation of large language models in mining gene relations and pathway knowledge.

Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large-scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)-based models and open-source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API-based models GPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged behind their API-based counterparts, whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh-aza).

PLRTE: Progressive learning for biomedical relation triplet extraction using large language models.

Document-level relation triplet extraction is crucial in biomedical text mining, aiding in drug discovery and the construction of biomedical knowledge graphs. Current language models face challenges in generalizing to unseen datasets and relation types in biomedical relation triplet extraction, which limits their effectiveness in these crucial tasks. To address this challenge, our study optimizes models from two critical dimensions: data-task relevance and granularity of relations, aiming to enhance their generalization capabilities significantly. We introduce a novel progressive learning strategy to obtain the PLRTE model. This strategy not only enhances the model's capability to comprehend diverse relation types in the biomedical domain but also implements a structured four-level progressive learning process through semantic relation augmentation, compositional instruction and dual-axis level learning. Our experiments on the DDI and BC5CDR document-level biomedical relation triplet datasets demonstrate a significant performance improvement of 5% to 20% over the current state-of-the-art baselines. Furthermore, our model exhibits exceptional generalization capabilities on the unseen Chemprot and GDA datasets, further validating the effectiveness of optimizing data-task association and relation granularity for enhancing model generalizability.

How to go green? Exploring public attention and sentiment towards waste sorting behaviors on Weibo platform: A study based on text co-occurrence networks and deep learning.

The attention and sentiment of the public are crucial for better implementation of waste sorting behaviors and moving towards green living. In this study, web scraping technology was used to collect 367,856 Weibo posts related to waste sorting from Sina Weibo. Utilizing text co-occurrence networks, Latent Dirichlet Allocation (LDA) topic modeling, and a deep learning model combining the Affective Cognitive Model (OCC) with Long Short-Term Memory Model (LSTM) (referred to as OCC-LSTM), we comprehensively understand the text at both micro and macro levels, analyzing the attention and sentiment of the public towards waste sorting behaviors on the Sina Weibo platform. Several important findings emerged from the empirical results. First, highly engaging posts were predominantly published by users with a large following, and the number of posts fluctuated over time. This reflects the influence of social hot topics and the timeliness of information dissemination. Second, there was heterogeneity in the user groups and their locations, often influenced by cultural differences due to geographical location. Third, positive sentiment towards waste sorting behavior was higher than negative sentiment on the Weibo platform. Moreover, public attention varied under different emotional influences concerning the topic of waste sorting behavior. The innovation of this study lies in the development of a research framework combining co-occurrence networks and deep learning, expanding the analysis on both micro and macro levels. This framework broadens the research paradigms and dimensions of public perception in waste sorting. This study is significant for promoting waste sorting behaviors and implementing climate policies.

Path analysis of coal mine accident risk factors based on the 24Model.

OBJECTIVES: Coal mine accidents seriously affect China's coal safety production and sustainable development. The present study aimed to reveal the risk factors in coal mine accidents and explore the causal relationship among risk factors. METHODS: This study utilized text mining to analyse 450 coal mine accident reports, identifying 50 risk factors and efficiently mapping them into the 24Model. The association rule algorithm was then used to mine the strong association rules among the risk factors within the 24Model, establishing the interaction mechanism among them. Based on the strong association rules, related hypotheses were proposed. Finally, the hierarchical and logical relationships of risk factors within the 24Model were analysed, and the causal and mediating effects were tested by path analysis. RESULTS: The safety management system has a direct effect on unsafe acts, unsafe conditions, habitual behaviour and organizational safety culture. Moreover, external influence has an effect on unsafe acts, organizational safety culture and habitual behaviour through the mediating effect of the safety management system. CONCLUSION: Based on the results obtained, this study proposes a series of specific measures to prevent risks in coal mines, providing a new perspective for the analysis and prevention of accidents.

Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis.

BACKGROUND: Allergy disorders caused by biological particles, such as the proteins in some airborne pollen grains, are currently considered one of the most common chronic diseases, and European Academy of Allergy and Clinical Immunology forecasts indicate that within 15 years 50% of Europeans will have some kind of allergy as a consequence of urbanization, industrialization, pollution, and climate change. OBJECTIVE: The aim of this study was to monitor and analyze the dissemination of information about pollen symptoms from December 2006 to January 2022. By conducting a comprehensive evaluation of public comments and trends on Twitter, the research sought to provide valuable insights into the impact of pollen on sensitive individuals, ultimately enhancing our understanding of how pollen-related information spreads and its implications for public health awareness. METHODS: Using a blend of large language models, dimensionality reduction, unsupervised clustering, and term frequency-inverse document frequency, alongside visual representations such as word clouds and semantic interaction graphs, our study analyzed Twitter data to uncover insights on respiratory allergies. This concise methodology enabled the extraction of significant themes and patterns, offering a deep dive into public knowledge and discussions surrounding respiratory allergies on Twitter. RESULTS: The months between March and August had the highest volume of messages. The percentage of patient tweets appeared to increase notably during the later years, and there was also a potential increase in the prevalence of symptoms, mainly in the morning hours, indicating a potential rise in pollen allergies and related discussions on social media. While pollen allergy is a global issue, specific sociocultural, political, and economic contexts mean that patients experience symptomatology at a localized level, needing appropriate localized responses. CONCLUSIONS: The interpretation of tweet information represents a valuable tool to take preventive measures to mitigate the impact of pollen allergy on sensitive patients to achieve equity in living conditions and enhance access to health information and services.

TopicTracker - An advanced software pipeline for text mining on PubMed data: Bridging the gap between off-the-shelf tools and code based approaches.

Background: The ever-increasing volume of academic literature necessitates efficient and sophisticated tools for researchers to analyze, interpret, and uncover trends. Traditional search methods, while valuable, often fail to capture the nuance and interconnectedness of vast research domains. Results: TopicTracker, a novel software tool, addresses this gap by providing a comprehensive solution from querying PubMed databases to creating intricate semantic network maps. Through its functionalities, users can systematically search for desired literature, analyze trends, and visually represent co-occurrences in a given field. Our case studies, including support for the WHO on ethical considerations in infodemic management and mapping the evolution of ethics pre- and post-pandemic, underscore the tool's applicability and precision. Conclusions: TopicTracker represents a significant advancement in academic research tools for text mining. While it has its limitations, primarily tied to its alignment with PubMed, its benefits far outweigh the constraints. As the landscape of research continues to expand, tools like TopicTracker may be instrumental in guiding scholars in their pursuit of knowledge, ensuring they navigate the large amount of literature with clarity and precision.

Quantitative Analysis of the Causes of Falls in Adult Hospitalized Patients Based on the Perspective of Text Mining.

OBJECTIVE: This study harnesses the power of text mining to quantitatively investigate the causative factors of falls in adult inpatients, offering valuable references and guidance for fall prevention measures within hospitals. METHODS: Employing KH Coder 3.0, a cutting-edge text mining software, we performed co-occurrence network analysis and text clustering on fall incident reports of 2,772 adult patients from a nursing quality control platform in a particular city in Jiangsu Province, spanning January 2017 to December 2022. RESULTS: Among the 2,772 patients who fell, 80.23% were aged above 60, and 73.27% exhibited physical frailty. Text clustering yielded 16 distinct categories, with four clusters implicating patient factors, four linking falls to toileting processes, four highlighting dynamic interplays between patients, the environment, and objects, and another four clusters revealing the influence of patient-caregiver interactions in causing falls. CONCLUSION: This study highlights the complex, multifactorial nature of falls in adult inpatients. Effective prevention requires a collaborative effort among healthcare staff, patients, and caregivers, focusing on patient vulnerabilities, environmental factors, and improved care coordination. By strengthening these aspects, hospitals can significantly reduce fall risks and promote patient safety.

Temporal Exploration of New Nurses' Field Adaptation Using Text Network Analysis.

PURPOSE: This study aimed to analyze the experiences of new nurses during their first year of hospital employment to gather data for the development of an evidence-based new nurse residency program focused on adaptability. METHODS: This study was conducted at a tertiary hospital in Korea between March and August 2021 with 80 new nurses who wrote in critical reflective journals during their first year of work. NetMiner 4.5.0 was used to conduct a text network analysis of the critical reflective journals to uncover core keywords and topics across three periods. RESULTS: In the journals, over time, degree centrality emerged as "study" and "patient understanding" for 1 to 3 months, "insufficient" and "stress" for 4 to 6 months, and "handover" and "preparation" for 7 to 12 months. Major sub-themes at 1 to 3 months were: "rounds," "intravenous-cannulation," "medical device," and "patient understanding"; at 4 to 6 months they were "admission," "discharge," "oxygen therapy," and "disease"; and at 7 to 12 months they were "burden," "independence," and "solution." CONCLUSION: These results provide valuable insights into the challenges and experiences encountered by new nurses during different stages of their field adaptation process. This information may highlight the best nurse leadership methods for improving institutional education and supporting new nurses' transitions to the hospital work environment.