Leveraging Reddit data for Context-enhanced Synthetic Health Data Generation to Identify Low Self Esteem.

Low self-esteem (LoST) is a latent yet critical psychosocial risk factor that predisposes individuals to depressive disorders. Although structured tools exist to assess self-esteem, their limited clinical adoption suggests that relevant indicators of LoST remain buried within unstructured clinical narratives. The scarcity of annotated clinical notes impedes the development of natural language processing (NLP) models for its detection. Manual chart reviews are labor-intensive and large language model (LLM)-driven (weak) labeling raises privacy concerns. Past studies demonstrate that NLP models trained on LLM-generated synthetic clinical notes achieve performance comparable to, and sometimes better than those trained on real notes. This highlights synthetic data’s utility for augmenting scarce clinical corpora while reducing privacy concerns. Prior efforts have leveraged social media data, such as Reddit, to identify linguistic markers of low self-esteem; however, the linguistic and contextual divergence between social media and clinical text limits the generalizability of these models. To address this gap, we present a novel framework that generates context-enhanced synthetic clinical notes from social media narratives and evaluates the utility of small language models for identifying expressions of low self-esteem. Our approach includes a mixed-method evaluation framework: (i) structure analysis, (ii) readability analysis, (iii) linguistic diversity, and (iv) contextual fidelity of LoST cues in source Reddit posts and synthetic notes. This work offers a scalable, privacy-preserving solution for synthetic data generation for early detection of psychosocial risks such as LoST and demonstrates a pathway for translating mental health signals in clinical notes into clinically actionable insights, thereby identifying patients at risk.

447 Background: Mortality from bladder cancer (BC) increases exponentially once it invades the muscle. At the population level, accurate delineation of these patients is challenging. Methods: To develop and validate a natural language processing (NLP) model for automatically identifying muscle-invasive BC (MIBC) patients, aiding in population-based BC research. All patients with a CPT code for transurethral resection of bladder tumor (TURBT) (N = 76,060) were selected from the Department of Veterans Affairs (VA) Corporate Data Warehouse database. A sample of 600 patients (with 2,337 full-text notes) who had TURBT and confirmed pathology results were selected for NLP model development (500 patients) and validation (100 patients). Muscle-invasion (yes/no), unknown, or no cancer, were confirmed by detailed chart review of pathology notes. The NLP performance was assessed by calculating the sensitivity, positive predictive value (PPV), and overall accuracy at the individual note and patient levels. Results: In the validation cohort, the NLP model had overall accuracy of 88% and 92% at the note and patient levels. Specifically, PPV and specificity for predicting muscle-invasion on note level were 83% and 70%, respectively. The model classified non-muscle invasive BC (NMIBC) with 98% sensitivity at both the note and patient levels. Although the sensitivity for MIBC was 70% for note-level determination, the sensitivity was 86% when evaluated at the patient level. When applying the model to 71,200 patients VA-wide, the model classified 13,642 (19%) as having MIBC and 47,595 (66%) as NMIBC. The NLP model was able to identify invasion status for 96% TURBT patients at the population level. Inherent limitations include relatively small training set given the size of the VA population. Conclusions: This NLP model for identifying muscle-invasion at the population level had high accuracy. The NLP model may be a practical and accurate tool for efficiently identifying BC invasion status and may potentially aid in population-based BC research in the VA.

Abstract. Large language models (LLMs) have revolutionized the field of natural language processing (NLP), demonstrating remarkable capabilities in understanding, generating, and manipulating human language. This comprehensive review explores the development, applications, optimizations, and challenges of LLMs. This paper begin by tracing the evolution of these models and their foundational architectures, such as the Transformer, GPT, and BERT. We then delve into the applications of LLMs in natural language understanding tasks, including sentiment analysis, named entity recognition, question answering, and text summarization, highlighting real-world use cases. Next, we examine the role of LLMs in natural language generation, covering areas such as content creation, language translation, personalized recommendations, and automated responses. We further discuss LLM applications in other NLP tasks like text style transfer, text correction, and language model pre-training. Subsequently, we explore techniques for optimizing and improving LLMs, including model compression, explainability, robustness, and security. Finally, we address the challenges posed by the significant computational requirements, sample inefficiency, and ethical considerations surrounding LLMs. We conclude by discussing potential future research directions, such as efficient architectures, few-shot learning, bias mitigation, and privacy-preserving techniques, which will shape the ongoing development and responsible deployment of LLMs in NLP.

Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation

BackgroundMortality is a critical variable in health care research, especially for evaluating medical product safety and effectiveness. However, inconsistencies in the availability and timeliness of death date and cause of death (CoD) information present significant challenges. Conventional sources such as the National Death Index and electronic health records often experience data lags, missing fields, or incomplete coverage, limiting their utility in time-sensitive or large-scale studies. With the growing use of social media, crowdfunding platforms, and web-based memorials, publicly available digital content has emerged as a potential supplementary source for mortality surveillance. Despite this potential, accurate tools for extracting mortality information from such unstructured data sources remain underdeveloped.ObjectiveThe aim of the study is to develop scalable approaches using natural language processing (NLP) and large language models (LLMs) for the extraction of mortality information from publicly available web-based data sources, including social media platforms, crowdfunding websites, and web-based obituaries, and to evaluate their performance across various sources.MethodsData were collected from public posts on X (formerly known as Twitter), GoFundMe campaigns, memorial websites (EverLoved and TributeArchive), and web-based obituaries from 2015 to 2022, focusing on US-based content relevant to mortality. We developed an NLP pipeline using transformer-based models to extract key mortality information such as decedent names, dates of birth, and dates of death. We then used a few-shot learning (FSL) approach with LLMs to identify primary and secondary CoDs. Model performance was assessed using precision, recall, F1-score, and accuracy metrics, with human-annotated labels serving as the reference standard for the transformer-based model and a human adjudicator blinded to the labeling source for the FSL model reference standard.ResultsThe best-performing model obtained a microaveraged F1-score of 0.88 (95% CI 0.86‐0.90) in extracting mortality information. The FSL-LLM approach demonstrated high accuracy in identifying primary CoD across various web-based sources. For GoFundMe, the FSL-LLM achieved 95.9% accuracy for primary cause identification compared to 97.9% for human annotators. In obituaries, FSL-LLM accuracy was 96.5% for primary causes, while human accuracy was 99%. For memorial websites, FSL-LLM achieved 98% accuracy for primary causes, with human accuracy at 99.5%.ConclusionsThis study demonstrates the feasibility of using advanced NLP and LLM techniques to extract mortality data from publicly available web-based sources. These methods can significantly enhance the timeliness, completeness, and granularity of mortality surveillance, offering a valuable complement to traditional data systems. By enabling earlier detection of mortality signals and improving CoD classification across large populations, this approach may support more responsive public health monitoring and medical product safety assessments. Further work is needed to validate these findings in real-world health care settings and facilitate the integration of digital data sources into national public health surveillance systems.

Understanding treatment patterns and effectiveness for patients with metastatic prostate cancer (mPCa) is dependent on accurate assessment of metastatic status. The objective was to develop a natural language processing (NLP) model for identifying patients with mPCa and evaluate the model's performance against chart-reviewed data and an International Classification of Diseases (ICD) 9/10 code-based method. In total, 139,057 radiology reports on 6,211 unique patients from the Department of Veterans Affairs were used. The gold standard was metastases by detailed chart review of radiology reports. NLP performance was assessed by sensitivity, specificity, positive predictive value, negative predictive value, and date of metastases detection. Receiver operating characteristic curves was used to assess model performance. When compared with chart review, the NLP model had high sensitivity and specificity (85% and 96%, respectively). The NLP model was able to predict patient-level metastasis status with a sensitivity of 91% and specificity of 81%, whereas sensitivity and specificity using ICD9/10 billing codes were 73% and 86%, respectively. For the NLP model, date of metastases detection was exactly concordant and within < 1 week in 55% and 58% of patients, compared with 8% and 17%, respectively, using the ICD9/10 billing codes method. The area under the curve for the NLP model was 0.911. A limitation is the NLP model was developed on the basis of a subset of patients with mPCa and may not be generalizable to all patients with mPCa. This population-level NLP model for identifying patients with mPCa was more accurate than using ICD9/10 billing codes when compared with chart-reviewed data. Upon further validation, this model may allow for efficient population-level identification of patients with mPCa.

Background and Aims Large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), marking a shift from traditional techniques like Term Frequency-Inverse Document Frequency (TF-IDF). We developed a traditional NLP model to predict arteriovenous fistula (AVF) failure within next 30 days using clinical notes. The goal of this analysis was to investigate whether LLMs would outperform traditional NLP techniques, specifically in the context of predicting AVF failure within the next 30 days using clinical notes. Method We defined AVF failure as the change in status from active to permanently unusable status or temporarily unusable status. We used data from a large kidney care network from January 2021 to December 2021. Two models were created using LLMs and traditional TF-IDF technique. We used “distilbert-base-uncased”, a distilled version of BERT base model [1], and compared its performance with traditional TF-IDF-based NLP techniques. The dataset was randomly divided into 60% training, 20% validation and 20% test dataset. The test data, comprising of unseen patients’ data was used to evaluate the performance of the model. Both models were evaluated using metrics such as area under the receiver operating curve (AUROC), accuracy, sensitivity, and specificity. Results The incidence of 30 days AVF failure rate was 2.3% in the population. Both LLMs and traditional showed similar overall performance as summarized in Table 1. Notably, LLMs showed marginally better performance in certain evaluation metrics. Both models had same AUROC of 0.64 on test data. The accuracy and balanced accuracy for LLMs were 72.9% and 59.7%, respectively, compared to 70.9% and 59.6% for the traditional TF-IDF approach. In terms of specificity, LLMs scored 73.2%, slightly higher than the 71.2% observed for traditional NLP methods. However, LLMs had a lower sensitivity of 46.1% compared to 48% for traditional NLP. However, it is worth noting that training on LLMs took considerably longer than TF-IDF. Moreover, it also used higher computational resources such as utilization of graphics processing units (GPU) instances in cloud-based services, leading to higher cost. Conclusion In our study, we discovered that advanced LLMs perform comparably to traditional TF-IDF modeling techniques in predicting the failure of AVF. Both models demonstrated identical AUROC. While specificity was higher in LLMs compared to traditional NLP, sensitivity was higher in traditional NLP compared to LLMs. LLM was fine-tuned with a limited dataset, which could have influenced its performance to be similar to that of traditional NLP methods. This finding suggests that while LLMs may excel in certain scenarios, such as performing in-depth sentiment analysis of patient data for complex tasks, their effectiveness is highly dependent on the specific use case. It is crucial to weigh the benefits against the resources required for LLMs, as they can be significantly more resource-intensive and costly compared to traditional TF-IDF methods. This highlights the importance of a use-case-driven approach in selecting the appropriate NLP technique for healthcare applications.

Social media analysis offers valuable insights into public health-related behaviours, such as attitudes toward vaccination and adherence to non-pharmaceutical interventions (e.g., mask-wearing), as well as determinants like trust in institutions and perceived risk. These digital traces help identify disinformation trends, public doubts, information needs, and behavioural signals - elements that could be integrated into behavioural models. The University of Pisa developed a structured social media monitoring approach through the RISP project (2023-2024), based on the WHO and UNICEF Infodemic Insights Reporting methodology. This system combines automated dashboards with manual narrative analysis, applied monthly to vaccine-related content. As an example, we will present insights from flu vaccine monitoring during the 2023-2024 season, which showed a rise in discouraging stances as institutional communication declined, alongside recurring questions on eligibility and safety. This monitoring approach forms the basis for ongoing work in the BEHAVE_MOD project, where social media data will be used to explore and model health-related behaviours across diseases such as flu, RSV, and Dengue. To make social media monitoring more directly usable in modelling frameworks, we are working to go beyond qualitative narrative identification by developing automated tools for stance classification. Specifically, we are building a framework that uses natural language processing (NLP) and large language models (LLMs) to classify vaccine stance at scale. This enables the generation of structured behavioural indicators-such as stance proportions over time-that can be quantified and integrated into mathematical models. While social media analysis cannot replace traditional behavioural surveillance systems, it can complement them by providing a low-cost, scalable, and near real-time data stream to support public health monitoring, communication, and modelling in routine and emergency settings.

Clinical predictive analysis is a crucial task with numerous applications and has been extensively studied using machine learning approaches. Clinical notes, a vital data source, have been employed to develop natural language processing (NLP) models for risk prediction in healthcare with robust performance. However, clinical notes vary considerably in text composition-written by diverse healthcare providers for different purposes-and the impact of these variations on NLP modeling is also underexplored. It also remains uncertain whether the recent Large Language Models (LLMs) with instruction-following capabilities can effectively handle the risk prediction task out-of-the-box, especially when using routinely collected clinical notes instead of polished text. We address these two important research questions in the context of in-hospital mortality prediction within the critical care setting. Specifically, we propose a supervised hierarchical network with note-specific modules to account for variations across different note categories, and provide a detailed comparison with strong supervised baselines and LLMs. We benchmark 34 instruction-following LLMs based on zero-shot, few-shot, and chain-of-thought prompting with diverse prompt templates. Our results demonstrate that the note-specific network delivers improved risk prediction performance compared to established supervised baselines from both measurement-based and text-based modeling. In contrast, LLMs consistently underperform on this critical task, despite their remarkable performances in other domains. This highlights important limitations and raises caution regarding the use of LLMs for risk assessment in the critical setting. Additionally, we show that the proposed model can be leveraged to select informative clinical notes to enhance the training of other models.

Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis and patient management. We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias. Of 1491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like Bidirectional Encoder Representations from Transformers and Generative Pre-trained Transformers. High accuracy was observed in detecting conditions such as RA, SpAs and gout. The use of NLP also showed promise in managing diseases and predicting flares. NLP showed significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like RA and gout are well developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice.

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance.

Medical educators pay increasing attention to the potential utility of narrative data for assessment, but lack of efficient and standardized ways of interpreting the data have limited its use. Natural language processing (NLP) algorithms could provide new insights for using narrative data within assessment especially identifying at-risk or low-performing students. Assessment data were reviewed from 16 cohorts of medical students from the University of Cincinnati College of Medicine (graduating classes of 2006-2022). A T-score Average (TSA) was calculated for each student based on clerkship assessment data. Narrative data from core clerkship evaluations in responses to the prompt "any opportunities for improvement" were utilized for analysis. The narrative data and calculated TSA were then used to train and test 4 NLP models with the goal of utilizing NLP to identify at-risk students as defined by the bottom 10% average TSA score. Based on typical NLP methods, the developed NLP models all performed adequately in identifying student performance with an overall accuracy of 0.8 across all 4 models. However, none of the NLP models was able to identify students within the bottom 10% of performance. During this process, we uncovered the presence of "copy/paste" behavior, a previously undocumented phenomenon within narrative data where preceptors duplicated comments from 1 student to the next. Training NLP models including "copy/paste" comments improved NLP ability to identify students within the bottom and top 10% of performance. NLP models were unable to accurately identify at-risk students. Model accuracy increased with the inclusion of "copy/paste" comments, indicating there may be some discriminatory functionality within aspects of narrative data beyond keywords such as lexical diversity and word quantity. Future work will explore how these other aspects of narrative data associate with performance and utilize more advanced large language models of analysis.Based on typical natural language processing (NLP) methods, the tested NLP models performed adequately in identifying student performance with an overall accuracy of 0.8 across all models. However, none of the NLP models was able to identify students within the bottom 10% of performance, revealing widespread "copy/paste" behavior in evaluations that may limit narrative data's discriminatory utility.

In light of the rapid expansion of maritime trade, the maritime transportation industry has experienced burgeoning growth and complexity. The deployment of trajectory prediction technology is paramount in safeguarding navigational safety. Due to limitations in design complexity and the high costs of data fusion, current deep learning methods struggle to effectively integrate high-level semantic cues, such as vessel type, geographical identifiers, and navigational states, within predictive frameworks. Yet, these data contain abundant information regarding vessel categories or operational scenarios. Inspired by the robust semantic comprehension exhibited by large language models (LLMs) in natural language processing, this study introduces a trajectory prediction method leveraging LLMs. Initially, Automatic Identification System (AIS) data undergoes processing to eliminate incomplete entries, thereby selecting trajectories of high quality. Distinct from prior research that concentrated solely on vessel position and velocity, this study integrates ship identity, spatiotemporal trajectory, and navigational information through prompt engineering, empowering the LLM to extract multidimensional semantic features of trajectories from comprehensive natural language narratives. Thus, the LLM can amalgamate multi-source semantics with zero marginal cost, significantly enhancing its understanding of complex maritime environments. Subsequently, a supervised fine-tuning approach rooted in Low-Rank Adaptation (LoRA) is applied to train the chosen LLMs. This enables rapid adaptation of the LLM to specific maritime areas or vessel classifications by modifying only a limited subset of parameters, thereby appreciably diminishing both data requirements and computational costs. Finally, representative metrics are utilized to evaluate the efficacy of the model training and to benchmark its performance against prevailing advanced models for ship trajectory prediction. The results indicate that the model demonstrates notable performance in short-term predictions fFor instance, with a prediction step of 1 h, the average distance errors for VTLLM and TrAISformer are 5.26 nmi and 6.12 nmi, respectively, resulting in a performance improvement of approximately 14.05%), having identified certain patterns and features, such as linear movements and turns, from the training data.

Evaluating gpt-4 for zero-shot classification of bleeding and clotting events: Can large language models serve as second reviewers?

Aim/Purpose: The study investigates the factors influencing the acceptance and utilisation of large language models (LLMs) (predictor variables of LLM usage), such as ChatGPT, in Learning design by instructional designers and university-teaching academics from various countries. Background: Large language models (LLMs) have exploded onto the scene, transforming the landscape of learning design. Instructional designers and university teaching academics have been overburdened with content creation for their teaching programmes, and the arrival of LLM models will help in this regard by developing more interactive content that drives student engagement and, in turn, contributes to student success. Since LLMs are a relatively new phenomenon, little is known about the factors influencing their acceptance in learning design; therefore, this research is needed, as learning design principles are the bedrock of student engagement and success. Methodology: A cross-sectional correlational quantitative study was employed. Data was collected using an online questionnaire posted on social media, including LinkedIn, from 203 instructional designers and university teaching academics. Purposive and snowball sampling methods were used to target instructional designers and university teaching academics at colleges and universities worldwide. Participants were asked to share the survey link with fellow instructional designers and university-teaching academics in their communities. The factor structure of the data was determined using exploratory factor analysis. Nonetheless, the factor structure derived from the LLMs did not entirely reflect the original configuration of the Unified Theory of Acceptance and Use of Technology (UTAUT3), as certain predictors appeared to coalesce, indicating LLMs’ unique nature in learning design. Confirmatory factor analysis was used to verify the fit of the data on the measurement model. First-order and second-order structural modelling were used to identify the structural relationships among the variables. Contribution: The study determines significant factors for the acceptance of LLMs by instructional designers and academic teaching staff in learning design, enabling possible opportunities for best practices in the field through interventions to optimize LLM usage. The study applies the technology acceptance model to the emerging LLM technology and extends the technology acceptance model by adding the trust construct as a predictor variable. Findings: The structural analysis results indicated that the ingrained LLM practices, LLM peer-driven expectations, innovative propensity towards LLM adoption, reliability and provider trust in LLMs, and ease of use and support influenced perceived LLM benefits and usage, but community standards and infrastructure had no influence. The second-order structural equation modelling indicated that perceived LLM benefits and usage and ingrained LLM habits contributed most to the learning design. Recommendations for Practitioners: Teaching academics and instructional designers must use LLMs in designing content, assessments, and interactive learning activities, and attend LLM training workshops on prompting and best practices in integrating LLMs into learning and teaching to see their benefits; hence, regular use of LLMs will then lead to trust and innovation in LLMs usage, enhancing learning design and improving student learning outcomes. Recommendation for Researchers: Researchers must use mixed methods approaches to have a deeper understanding of the factors influencing LLMs. Since habit and perceived LLM benefits and usage contributed the most variance to learning design, researchers must investigate strategies that optimise these factors in learning design, such as effective intervention strategies that can help form positive LLM habits. In addition, the findings provide researchers with a starting point for future research. Further researchers must investigate interventions that optimise the influence of personal innovativeness and trust that contributed the least variance to learning design, hence unlocking the potential of LLMs in learning design through innovation, responsible, and ethical use. Impact on Society: The use of LLMs in learning design has a high possibility of transforming education, specifically the learning design landscape. Using LLMs will free up more time for teaching academics and instructional designers so that they spend more time on higher-order thinking skill demands. Consequently, the students will be exposed to more engaging and interactive content, resulting in improved learning outcomes. Future Research: Future research must include context-derived external variables in technology acceptance models, such as levels of prompting competencies, to provide a deeper understanding of LLMs. In addition, future research must be based on the application and impact of LLMs on student engagement and success, and their attainment of 21st-century skills.