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.

A method combining LDA and neural networks for antitumor drug efficacy prediction.

Background: Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task. Objective: This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data. Methods: This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network to predict drug response in cancer patients treated with platinum drugs. To evaluate the effectiveness of the proposed method, we gathered and organized clinical records of lung and bowel cancer patients who underwent platinum-based treatment. The prediction performance is assessed using the following metrics: Precision, Recall, F1-score, Accuracy, and Area Under the ROC Curve (AUC). Results: The study analyzed a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. The proposed method achieved a stratified 5-fold cross-validation average Precision of 0.81, Recall of 0.89, F1-score of 0.85, Accuracy of 0.77, and AUC of 0.81 for cisplatin efficacy prediction on the data, which most are better than those of previous methods. Of these, the AUC value is at least 4% higher than those of the previous. At the same time, the superior result over the previous method persisted on an independent dataset of 266 bowel cancer patients, showing the generalizability of the proposed method. These results demonstrate the potential value of precise tumor treatment in clinical practice. Conclusions: Combining LDA and neural networks can help predict the efficacy of antitumor drugs based on clinical text. Our approach outperforms previous methods in predicting drug clinical efficacy.

Analysis of child development facts and myths using text mining techniques and classification models.

The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequences, such as inappropriate treatment of children based on myths. While previous research has utilized text-mining techniques to predict child abuse cases, there has been a gap in the analysis of child development myths and facts. This study addresses this gap by applying text mining techniques and classification models to distinguish between myths and facts about child development, leveraging newly gathered data from publicly available websites. The research methodology involved several stages. First, text mining techniques were employed to pre-process the data, ensuring enhanced accuracy. Subsequently, the structured data was analysed using six robust Machine Learning (ML) classifiers and one Deep Learning (DL) model, with two feature extraction techniques applied to assess their performance across three different training-testing splits. To ensure the reliability of the results, cross-validation was performed using both k-fold and leave-one-out methods. Among the classification models tested, Logistic Regression (LR) demonstrated the highest accuracy, achieving a 90 % accuracy with the Bag-of-Words (BoW) feature extraction technique. LR stands out for its exceptional speed and efficiency, maintaining low testing time per statement (0.97 µs). These findings suggest that LR, when combined with BoW, is effective in accurately classifying child development information, thus providing a valuable tool for combating misinformation and assisting parents in making informed decisions.

Analysis of Guangzhou city image perception based on weibo text data (2019-2023).

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to "Guangzhou City" between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

The landscape of miRNA-mRNA regulatory network and cellular sources in inflammatory bowel diseases: insights from text mining and single cell RNA sequencing analysis.

Background: Inflammatory Bowel Diseases (IBDs), encompassing Ulcerative Colitis (UC) and Crohn's Disease (CD), are chronic, recurrent inflammatory conditions of the gastrointestinal tract. The microRNA (miRNA) -mRNA regulatory network is pivotal in the initiation and progression of IBDs. Although individual studies provide valuable insights into miRNA mechanisms in IBDs, they often have limited scope due to constraints in population diversity, sample size, sequencing platform variability, batch effects, and potential researcher bias. Our study aimed to construct comprehensive miRNA-mRNA regulatory networks and determine the cellular sources and functions of key miRNAs in IBD pathogenesis. Methods: To minimize potential bias from individual studies, we utilized a text mining-based approach on published scientific literature from PubMed and PMC databases to identify miRNAs and mRNAs associated with IBDs and their subtypes. We constructed miRNA-mRNA regulatory networks by integrating both predicted and experimentally validated results from DIANA, Targetscan, PicTar, Miranda, miRDB, and miRTarBase (all of which are databases for miRNA target annotation). The functions of miRNAs were determined through gene enrichment analysis of their target mRNAs. Additionally, we used two large-scale single-cell RNA sequencing datasets to identify the cellular sources of miRNAs and the association of their expression levels with clinical status, molecular and functional alternation in CD and UC. Results: Our analysis systematically summarized IBD-related genes using text-mining methodologies. We constructed three comprehensive miRNA-mRNA regulatory networks specific to IBD, CD, and UC. Through cross-analysis with two large-scale scRNA-seq datasets, we determined the cellular sources of the identified miRNAs. Despite originating from different cell types, hsa-miR-142, hsa-miR-145, and hsa-miR-146a were common to both CD and UC. Notably, hsa-miR-145 was identified as myofibroblast-specific in both CD and UC. Furthermore, we found that higher tissue repair and enhanced glucose and lipid metabolism were associated with hsa-miR-145 in myofibroblasts in both CD and UC contexts. Conclusion: This comprehensive approach revealed common and distinct miRNA-mRNA regulatory networks in CD and UC, identified cell-specific miRNA expressions (notably hsa-miR-145 in myofibroblasts), and linked miRNA expression to functional alterations in IBD. These findings not only enhance our understanding of IBD pathogenesis but also offer promising diagnostic biomarkers and therapeutic targets for clinical practice in managing IBDs.

BERT4Cache: a bidirectional encoder representations for data prefetching in cache.

Cache plays a crucial role in improving system response time, alleviating server pressure, and achieving load balancing in various aspects of modern information systems. The data prefetch and cache replacement algorithms are significant factors influencing caching performance. Due to the inability to learn user interests and preferences accurately, existing rule-based and data mining caching algorithms fail to capture the unique features of the user access behavior sequence, resulting in low cache hit rates. In this article, we introduce BERT4Cache, an end-to-end bidirectional Transformer model with attention for data prefetch in cache. BERT4Cache enhances cache hit rates and ultimately improves cache performance by predicting the user's imminent future requested objects and prefetching them into the cache. In our thorough experiments, we show that BERT4Cache achieves superior results in hit rates and other metrics compared to generic reactive and advanced proactive caching strategies.

Mental health issues of higher education students reflected in academic research: A text mining study.

Objective: This study investigated mental health issues among higher education students to identify key concepts, topics, and trends over three periods of time: Period 1 (2000-2009), Period 2 (2010-2019), and Period 3 (2020-May 2024). Methods: The study collected 11,732 bibliographic records from Scopus and Web of Science, published between January 2000 and May 2024, and employed textual analysis methods, including keyword co-occurrence analysis, cluster analysis, and topic modeling. Results: In Period 1, general topics related to mental health disorders and treatments were identified. Period 2 showed prominence of well-being and help-seeking, as well as the emergence of digital mental health. Period 3 emphasized the impact of COVID-19 and increased technology usage. Conclusions: Based on the findings, we discussed the significance of the study and practical implications for clinicians and policymakers, as well as methodological implications for researchers. Additionally, the limitations of the study and future research were addressed.

Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines.

Complete and transparent reporting of randomized controlled trial publications (RCTs) is essential for assessing their credibility. We aimed to develop text classification models for determining whether RCT publications report CONSORT checklist items. Using a corpus annotated with 37 fine-grained CONSORT items, we trained sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. We assessed the impact of data augmentation methods (Easy Data Augmentation (EDA), UMLS-EDA, text generation and rephrasing with GPT-4) on model performance. We also fine-tuned section-specific PubMedBERT models (e.g., Methods) to evaluate whether they could improve performance compared to the single full model. We performed 5-fold cross-validation and report precision, recall, F1 score, and area under curve (AUC). Fine-tuned PubMedBERT model that uses the sentence along with the surrounding sentences and section headers yielded the best overall performance (sentence level: 0.71 micro-F1, 0.67 macro-F1; article-level: 0.90 micro-F1, 0.84 macro-F1). Data augmentation had limited positive effect. BioGPT fine-tuning and GPT-4 in-context learning exhibited suboptimal results. Methods-specific model improved recognition of methodology items, other section-specific models did not have significant impact. Most CONSORT checklist items can be recognized reasonably well with the fine-tuned PubMedBERT model but there is room for improvement. Improved models can underpin the journal editorial workflows and CONSORT adherence checks.

A survey on the reasons why victims of stalking did not exhibit help-seeking behavior: a text-mining analysis.

BACKGROUND: Stalking can escalate into violent acts such as threatening and inflicting physical harm, posing a serious threat to personal safety. To prevent exacerbating stalking victimization, victims must seek help and report incidents to the police or relevant authorities. However, victims, in general, underreport these incidents to public institutions. Moreover, there is insufficient understanding of why victims of stalking, especially men, refrain from seeking help. Therefore, this study used text mining to explore the reasons victims of stalking in Japan do not seek help while considering the severity of victimization and analyzing data separately for men and women. METHODS: Among 908 Japanese individuals who reported experiencing repeated stalking behavior from a former intimate partner in the past five years, 253 men and 321 women who did not consult public authorities were included in this study. Participants provided their experiences of being stalked by former romantic partners and were classified into stalking-only, threatened, and physical aggression victim groups based on their self-reported experiences in an online survey. Reasons for not seeking help were collected through open-ended questions and analyzed using text mining. RESULTS: A co-occurrence network analysis revealed that among men in the threatened victim group, the reason for not seeking help was the belief that their complaints would not be taken seriously. The physical aggression victim group did not seek help due to the perception that a female perpetrator does not pose a danger. Among women in the physical aggression victim group, concerns about provoking the perpetrator or worsening the situation by seeking help, as well as feelings of embarrassment, were reasons for not seeking assistance. CONCLUSIONS: The identification of gender stereotype-related reasons among male victims was a valuable insight that could only be obtained through comparison with female victims. However, the study was limited to addressing the individual characteristics of the cases, thus providing only hypothetical insights into general trends. In future research, it will be necessary to generate hypotheses from the findings of this study and accumulate hypothesis-testing research to develop effective strategies for promoting help-seeking behavior among stalking victims.

State of infection prevention knowledge among healthcare professionals in Japan: a questionnaire survey analysis using text mining.

BACKGROUND: Healthcare-associated infections (HAIs) are an important issue that needs to be continuously addressed in healthcare institutions, and healthcare professionals (HCPs) are expected to strengthen educational programmes on infection prevention. Although the incidence of HAIs in Japan has been decreasing, the actual state of knowledge on infection prevention among HCPs remains unclear. AIM: To clarify the actual infection prevention knowledge of HCPs in Japan. METHODS: The study participants were 1158 HCPs working in healthcare institutions with frequent contact with patients (283 doctors, 591 nurses, 115 physical therapists, 97 radiologists, and 72 medical technologists). HCPs described the infection prevention behaviours they consciously adhered to via an online self-administered questionnaire. Data were analysed by text mining. Categories were extracted from the responses to reveal HCPs' infection prevention knowledge. FINDINGS: More than half of the participants (64.9%) were aged > 40 years, and 48.1% had over 20 years of clinical experience. The majority of the participants were nurses (51.0%), 43.9% had a bachelor's degree, and 56.6% were female. Seven categories regarding infection prevention knowledge were extracted: "performing hand hygiene and gargling," "wearing personal protective equipment," "strengthening one's immunity," "protecting oneself and patients from infection," "distinguishing clean and unclean zones," "actions to prevent transmission to others in daily life activities," and "maintaining distance from others." CONCLUSION: These results suggest that most HCPs working in healthcare settings in Japan prioritize and adhere to standard precautionary measures, and that the low incidence of HAIs may be influenced by perceptions of the knowledge of "handwashing and gargling" among HCPs.

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, certain countries have high suicide mortality rates (SMRs) compared to others. Age-standardized suicide mortality rates (SMRs) published by the World Health Organization (WHO) plus numerous bibliographic records of the Web of Science (WoS) database provide resources to understand these disparities between countries and regions. (2) Methods: Hierarchical clustering was applied to age-standardized suicide mortality rates per 100,000 population from 2000-2019. Keywords of country-specific suicide-related publications collected from WoS were analyzed by network and association rule mining. Keyword embedding was carried out using a recurrent neural network. (3) Results: Countries with similar SMR trends formed naturally distinct groups of high, medium, and low suicide mortality rates. Major themes in suicide research worldwide are depression, mental disorders, youth suicide, euthanasia, hopelessness, loneliness, unemployment, and drugs. Prominent themes differentiating countries and regions include: alcohol in post-Soviet countries; HIV/AIDS in Sub-Saharan Africa, war veterans and PTSD in the Middle East, students in East Asia, and many others. (4) Conclusion: Countries naturally group into high, medium, and low SMR categories characterized by different keyword-informed themes. The compiled dataset and presented methodology enable enrichment of analytical results by bibliographic data where observed results are difficult to interpret.

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.

Analysis on typical characteristics and causes of coal mine gas explosion accidents in China.

Large-scale coal mine gas explosion (CMGE) accidents have occurred occasionally and exerted a devastating effect on society. Therefore, it is essential to systematically identify the characteristics and association rules of causes of CMGE accidents through analysis on large-scale CMGE accident reports. In this study, 298 large-scale CMGE accidents in China from 2000 to 2021 were taken as the data sample, and mathematical statistical methods were adopted to analyze their general characteristics, coupling cross characteristics, and characteristics of gas accumulation and ignition sources. Moreover, the text mining technology and the Apriori algorithm were used for exploring the formation mechanism of CMGE accidents, during which 46 main causal factors were identified and 59 strong association rules were obtained. Furthermore, an accident causation network was constructed based on the co-occurrence matrix. The key causal items and sets of CMGE accidents were clarified through network centrality analysis. According to the research results, electrical equipment failure, cable short circuit, mine lamp misfire, hot-line work, and blasting spark are the key ignition sources of CMGE. Fan failure, airflow short circuit, and local ventilation fan damage are the main causes of gas accumulation. Besides, the confidence levels of two association rules of "static spark-fan failure" and "blasting spark-airflow short circuit" are higher than 70%, indicating that they are the two dominant risk-coupling paths of gas explosions. In addition, six causes appear frequently in the shortest risk paths of gas explosion and are closely related to other causes, i.e., fan failure, local ventilation fan damage, static sparks, electrical equipment failure, self-heating ignition, and friction impact sparks. This study provides a new perspective on identifying causes of accidents and their complex association mechanisms from accident report data for practical guidance in risk assessment and accident prevention.

State of the art of the cow-calf systems in beef and dairy cattle (Bos taurus) operations in EU, USA, and Brazil from 1998 to 2023.

Cow-calf systems represent a significant research area in animal husbandry, with differences depending on the final product (meat or milk). This study aimed to apply text mining and topic analysis on literature describing cow-calf systems in European, American, and Brazilian beef and dairy sectors between 1998 and 2023. Additionally, cow-calf contact (CCC) literature data was manually extracted. Our findings revealed the presence of 11 research areas among literature on cow-calf systems, with different priorities identified in the beef and dairy sectors. Beef industry mainly focused on animal proficiency and nutrition, while dairy on animal welfare and CCC, which showed a growing trend as emerging research topic, mostly in the EU. Current debates around calf welfare and EU's planned animal welfare legislation revision appeared to be driving the increasing interest in this topic. Studies in the beef sector were mainly localized in Brazil, showing that research in different contexts and species is important for CCC implementation. Manual data extraction showed considerable variation in the retained CCC documents regarding sample size, type of contact, methods and CCC duration. Learning about the varied CCC approaches used in beef and dairy farms in different locations, concentrating on their strengths and weaknesses, will help to develop novel solutions to global challenges. Adopting validated and robust indicators would help scientists and policymakers to monitor the system's quality. To improve CCC feasibility, match consumer demands, and move towards One Welfare and One Health, future research should focus on a variety of situations to overcome the current shortcomings.

Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challenges.

In recent years, e-commerce platforms have become popular and transformed the way people buy and sell goods. People are rapidly adopting Internet shopping due to the convenience of purchasing from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses increasingly rely on online reviews to assess and improve the quality of products. Existing literature uses natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis and opinion mining, review analysis and management, customer experience and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey also addresses open issues, providing future directions in analyzing online customer reviews.

Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach.

A diversity of contaminants of emerging concern (CECs) are present in wastewater effluent, posing potential threats to receiving waters. It is urgent for a holistic assessment of the occurrence and risk of CECs related to wastewater treatment plants (WWTP) on national and regional scales. A data mining-based risk prioritization method was developed to collect the reported contaminants and their respective concentrations in municipal and industrial WWTPs and their receiving waters across China over the past 20 years. A total of 10,781 chemicals were reported in 8336 publications, of which 1037 contaminants were reported with environmental concentrations. While contaminant categories varied across WWTP types (municipal vs. industrial) and regions, pharmaceuticals and cyclic hydrocarbons were the most studied CECs. Contaminant composition in receiving water was closer to that in municipal than industrial WWTPs. Publications on legacy pesticides and polycyclic aromatic hydrocarbons in WWTP decreased recently compared to the past, while pharmaceuticals and perfluorochemicals have received increasing attention, showing a changing concern over time. Detection frequency, concentration, removal efficiency, and toxicity data were integrated for assessing potential risks and prioritizing CECs on national and regional scales using an environmental health prioritization index (EHPi) approach. Among 666 contaminants in municipal WWTP effluent, trichlorfon and perfluorooctanesulfonic acid were with the highest EHPi scores, while 17ɑ-ethinylestradiol and bisphenol A had the highest EHPi scores among 304 contaminants in industrial WWTPs. The prioritized contaminants varied across regions, suggesting a need for tailoring regional measures of wastewater treatment and control.

Cancer Prevention and Treatment on Chinese Social Media: Machine Learning-Based Content Analysis Study.

BACKGROUND: Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment. A growing body of research has focused on assessing access and communication effects of cancer information on social media. However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methods across social media platforms. Furthermore, research comparing the differences between medical social media (MSM) and common social media (CSM) is also lacking. OBJECTIVE: Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informed treatment decisions. METHODS: We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medical backgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessional medical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-related posts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to our codebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task and utilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe), to learn document-level semantic representations for labeling. RESULTS: We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52% (2993/8427) contained prevention information and 44.43% (3744/8427) contained treatment information. Themes in CSM were predominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned prevention measures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer prevention more frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention. CONCLUSIONS: The information about cancer prevention and treatment on social media revealed a lack of balance. The focus was primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in social media. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promising research approach for mapping key dimensions of cancer information on social media. These findings hold methodological and practical significance for future studies and health promotion.

Temporal evolution and thematic shifts in sustainable construction and demolition waste management through building information modeling technologies: A text-mining analysis.

Construction and demolition activities are significant contributors to waste generation worldwide. As population growth accelerates worldwide, the amount of construction and demolition waste (C&DW) will increase proportionally unless proactive measures are implemented. This study analyzes the evolving research landscape on utilizing Building Information Modeling (BIM) technologies to advance sustainable C&DW management practices. A comprehensive text-mining analysis is conducted on 493 scholarly publications covering evolutions from January 2009 to February 2024 using the PRISMA framework. The research objectives are: (i) to identify key themes in domain of BIM technology in C&DW management using VOSviewer, (ii) to map the temporal evolution of research focus using SciMAT, and (iii) to identify emerging thematic trends.Co-occurrence analysis reveals three major research themes: (i) the use of digital twins and prefabrication for waste reduction, (ii) integrating environmental impact assessments, and (iii) data-driven decision-making. Strategic diagrams produced by SciMAT software uncover shifting priorities over the study period, with "reuse and recycling" emerging as motor themes, and "Prefabrication" (CIT = 481), "Decision Making" (CIT = 66), "Material Passport" (CIT = 92), and "Digital Twin" (CIT = 44) emerging as high-centrality and transversal themes. Temporal evolution mapping unveiled progressive integration of BIM tools such as (i) digital twins (TLS = 34, OCC = 9) and (ii) prefabrication (TLS = 40, OCC = 14), presenting opportunities to optimize waste reduction. This study offers a robust overview of the field, aiming to inform a diverse audience, including researchers from various disciplines, policymakers and industry professionals interested in advancing sustainable practices in C&DW management through innovative digital solutions.