Natural Language Processing Models Research Articles

Understanding urban perception with visual data: A systematic review

Visual characteristics of the built environment affect how people perceive and experience cities. For a long time, many studies have examined visual perception in cities. Such efforts have accelerated in recent years due to advancements in technologies and the proliferation of relevant data (e.g., street view imagery, geo-tagged photos, videos, virtual reality, and aerial imagery). There has not been a comprehensive systematic review paper on this topic to reveal an overarching set of research trends, limitations, and future research opportunities. Such omission is plausibly due to the difficulty in reviewing a large number of relevant papers on this popular topic. In this study, we utilized machine learning techniques (i.e., natural language processing and large language models) to semi-automate the review process and reviewed 393 relevant papers. Through the review, we found that these papers can be categorized into the physical aspects of cities: greenery and water, street design, building design, landscape, public space, and the city as a whole. We also revealed that many studies conducted quantitative analyses with a recent trend of increasingly utilizing big data and advanced technologies, such as combinations of street view imagery and deep learning models. Limitations and research gaps were also identified as follows: (1) a limited scope in terms of study areas, sample size, and attributes; (2) low quality of subjective and visual data; and (3) the need for more controlled and sophisticated methods to infer more closely examined impacts of visual features on human perceptions. We suggest that future studies utilize and contribute to open data and take advantage of existing data and technologies to examine the causality of visual features on human perception. The approach developed to accelerate this review proved to be accurate, efficient, and insightful. Considering its novelty, we also describe it to enable replications in the future.

The propensity for negative media reporting of the Murray-Darling Basin Plan in Australia

The Murray-Darling Basin (MDB) Plan, or simply ‘Basin Plan’, represents one of the world's largest returns from consumptive (irrigation) water extraction to environmental use. The Plan has been mired in controversy from when its first draft was burned by irrigators in 2010, and as such has been a source of extensive media reporting. Given the ability of journalism in both informing and influencing public debates, as well as the political deliberation process, we sought to understand the themes and representation of the Plan in media newspapers. This study analysed 6694 media articles referring to the Basin Plan, across 135 outlets, from 2008 to 2022. All articles were sentiment coded using four different natural language processing (NLP) models, with additional manual coding of randomly selected 552 articles. Three-quarters of the total articles were coded as negative using the best-fitting DistilBERT NLP sentiment analysis, while the manual coding suggested almost half were negative in tone. Both methods found that negative stories far outweighed positive stories, suggesting very little ‘balance’ in media Basin Plan reporting. A regression analysis found that rural media outlets were more likely to report negative MDB stories, and that the states of South Australia and Queensland were more likely to report positive stories. Themes of irrigator victimisation and community economic decline dominated news articles (especially negative stories); followed by Basin Plan process, outcomes and governance issues; fights between states; and environmental issues. Environmental themes were relatively less discussed, and Indigenous issues were only referred to in a tiny fraction of all media stories. The study's results provide evidence that the media plays critical roles in water policy debates, giving prominent coverage - with little critical discernment of actual economic impacts - to certain actors with clear ideological and vested interests in the outcomes. Far less media coverage was given to more complex issues such as climate change, long-term structural change for irrigation communities, ecological crises, and sharing water with those who have little.

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD-code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. The model development cohort (n=20000: training, 16000; validation 2000; testing, 2000) included 10000 patients with HF and 10000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP-HF) and non-HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF-related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP-HF as cases (PPV, 0.86; sensitivity, 0.86). From the 'HF ICD-code universe', we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP+ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP+ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.

Towards human-AI collaboration in the competency-based curriculum development process: The case of industrial engineering and management education

In the endeavor to advance industrial engineering and management (IEM) education, this research underscores the imperative of supporting a dynamic and responsive adaptation of a competency-based curriculum (CBC) to meet the demands of an ever-evolving industrial landscape and job market. Our study contributes to competency-based education (CBE) by demonstrating how Artificial Intelligence (AI) can inform the definition of a CBC in the IEM field, thus initiating the pioneering steps towards a collaborative human-AI approach in CBC design. Through a stepwise methodology based on semantic analysis, text mining, natural language processing (NLP) models, informetrics approaches, and clustering algorithms, we provide data-driven insights to inform the curriculum development process. This approach enabled us to identify educational gap, particularly in domains such as digital twin engineering and human-centric IEM. Moreover, this study advocates for higher education institutions (HEIs) to embrace a more structured and collaborative approach to continuously developing competency-based curricula. In this perspective, AI (including generative AI) emerges as a valuable ally in curriculum design. This approach proves instrumental in crafting competitive and appealing curricula, especially at peripheral universities. This study culminates in an updated WING model showing how to build Industry 5.0 related curricula and a series of recommendations for engineering educators.

A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring.

Investigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts. For this purpose, we integrate large language models into the Manubot publishing ecosystem to suggest revisions for scholarly texts. Our AI-based revision workflow employs a prompt generator that incorporates manuscript metadata into templates, generating section-specific instructions for the language model. The model then generates revised versions of each paragraph for human authors to review. We evaluated this methodology through 5 case studies of existing manuscripts, including the revision of this manuscript. Our results indicate that these models, despite some limitations, can grasp complex academic concepts and enhance text quality. All changes to the manuscript are tracked using a version control system, ensuring transparency in distinguishing between human- and machine-generated text. Given the significant time researchers invest in crafting prose, incorporating large language models into the scholarly writing process can significantly improve the type of knowledge work performed by academics. Our approach also enables scholars to concentrate on critical aspects of their work, such as the novelty of their ideas, while automating tedious tasks like adhering to specific writing styles. Although the use of AI-assisted tools in scientific authoring is controversial, our approach, which focuses on revising human-written text and provides change-tracking transparency, can mitigate concerns regarding AI's role in scientific writing.

Google Bard and ChatGPT in Orthopedics: Which Is the Better Doctor in Sports Medicine and Pediatric Orthopedics? The Role of AI in Patient Education.

This study evaluates the potential of ChatGPT and Google Bard as educational tools for patients in orthopedics, focusing on sports medicine and pediatric orthopedics. The aim is to compare the quality of responses provided by these natural language processing (NLP) models, addressing concerns about the potential dissemination of incorrect medical information. Ten ACL- and flat foot-related questions from a Google search were presented to ChatGPT-3.5 and Google Bard. Expert orthopedic surgeons rated the responses using the Global Quality Score (GQS). The study minimized bias by clearing chat history before each question, maintaining respondent anonymity and employing statistical analysis to compare response quality. ChatGPT-3.5 and Google Bard yielded good-quality responses, with average scores of 4.1 ± 0.7 and 4 ± 0.78, respectively, for sports medicine. For pediatric orthopedics, Google Bard scored 3.5 ± 1, while the average score for responses generated by ChatGPT was 3.8 ± 0.83. In both cases, no statistically significant difference was found between the platforms (p = 0.6787, p = 0.3092). Despite ChatGPT's responses being considered more readable, both platforms showed promise for AI-driven patient education, with no reported misinformation. ChatGPT and Google Bard demonstrate significant potential as supplementary patient education resources in orthopedics. However, improvements are needed for increased reliability. The study underscores the evolving role of AI in orthopedics and calls for continued research to ensure a conscientious integration of AI in healthcare education.

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language understanding (NLU), dialogue management, and natural language generation (NLG). We designed and implemented an HRI system that leverages models such as BERT for language understanding and GPT-3 for generating contextually appropriate responses. Our methodology involves integrating these NLP models with a robotics platform, ensuring real-time interaction capabilities while maintaining a high level of accuracy and context awareness. The system was evaluated through a series of user studies, measuring performance metrics such as accuracy, latency, and user satisfaction. Results indicate that our NLP-enhanced HRI system significantly improves the quality of interactions, demonstrating superior understanding and responsiveness compared to traditional systems. This paper discusses the implementation challenges, including computational constraints and ambiguity resolution, and provides insights into user feedback and system performance. Future work will focus on enhancing context management, exploring multimodal interaction, and addressing ethical considerations in deploying advanced HRI systems. Our findings underscore the potential of NLP to transform human-robot communication, paving the way for more intuitive and effective robotic assistants in various domains. Keywords: Human-Robot Interaction (HRI), Natural Language Processing (NLP), Conversational AI, Speech Recognition, Natural Language Understanding (NLU), Natural Language Generation (NLG), Multimodal Interaction, Dialogue Systems, Context Awareness, Emotion Recognition, Machine Learning in HRI, Personalized Interaction, User Experience (UX) in HRI, Human-Centered Design, Collaborative Robots (Cobots)

USING NATURAL LANGUAGE PROCESSING (NLP) FOR CATEGORIZING PAPER TITLES FROM GOOGLE FORMS

<p>Modern data collection, storage, and processing rely on diverse techniques to handle various types of information, ranging from structured tables to free-form text. This paper explores the captivating application of Natural Language Processing (NLP) for categorizing titles from Google Forms or any other textual data. The process of training an NLP model will be demonstrated through a specific example. Just as we learn from our past experiences, NLP models need to be fed with relevant data and labels. This ensures accurate and efficient processing even when new titles are introduced. We will conclude with a fascinating demonstration of how NLP algorithms analyze the structure and meaning of titles. By identifying keywords and understanding the context, they can automatically classify titles into relevant categories. This dramatically simplifies data organization and analysis, empowering us to extract valuable insights faster.</p>

Artificial intelligence and management education: A conceptualization of human-machine interaction

The increasing use of Advanced Natural Language Processing (ANLP) models, particularly ChatGPT-4, presents opportunities and challenges to management education and research. These models can enhance the style, creativity, and analytical power of research papers, potentially shifting human scholars' roles from creators to ‘prompters’. If machines can perform educational and research tasks more effectively the role of human educators becomes a salient question in a world in which ANLP models offer clear, coherent, and polished insights, the use of which has potentially paradoxical possibilities. From one perspective, a new type of high-quality scholarship and education characterized by strong human involvement that synergistically leverages ANLP models' analytical capabilities, enabling human scholars to probe complex phenomena and make management research truly meaningful and impactful for broader audiences, is possible. We explore these questions through an ‘ideal type’ conceptualization of the possible relations between AI and management education and research.

Natural language processing and stable diffusion model based graphical authentication using passphrase

Graphical passwords or passphrases (GPs) are examined as reliable authentication system over text-based passphrases. Several obtainable applications utilize GPs and image-based authentication schemes. Even though, these authentication methods experience an issue of managing image or pictorial data. Here, Natural Language Processing (NLP)+Stable Diffusion (SD)_graphical authentication is presented for graphical authentication using passphrase. The passphrase, removing stop words, spell check, similarity check, stemming and lemmatization, label encoder, positional embedding and encryption are the steps carried out in registration phase whereas decryption Advanced Encryption Standard (AES), sentence generation, image set generation and grid module are conducted in verification phase. Initially, passphrase is considered and then, stop words are removed from it. Thereafter, spell check as well as similarity check are done and after these processes stemming and lemmatization is performed. In label encoder, label is generated for individual word in passphrase. Afterwards, positional embedding is done and lastly, encryption is conducted in registration phase. In verification phase, encrypted passphrase is decrypted, sentence is generated utilizing Generative Pre-trained Transformer (GPT), image set is generated by SD model and finally, image grid module is accomplished. Moreover, NLP+SD_graphical authentication achieved minimum attack detection, login failure, login time about 0.598, 0.191, 14.175sec and maximum memorability about 0.932.

Real-Time Stock Forecasting: Leveraging Live Data and Advanced Algorithms for Accurate Predictions

This groundbreaking research introduces an innovative stock market prediction methodology that integrates financial modeling, machine learning, and real-time data analysis. Anchored in a deep understanding of stock market dynamics, including economic statistics, company performance, and market sentiment, our approach employs natural language processing (NLP) and predictive modeling to analyze live data for accurate stock price predictions. The method involves collecting and preprocessing a dynamic dataset enriched with financial indicators and historical stock prices. Utilizing Long Short-Term Memory (LSTM) algorithms, our model exhibits an impressive 96% accuracy in forecasting stock movements, showcasing adaptability to diverse market scenarios and responsiveness to economic factors and sentiment shifts. The incorporation of live data proves pivotal in providing timely insights for informed decision-making, establishing our model as a valuable tool for navigating the complexities of the modern financial landscape.

LLMs and NLP Models in Cryptocurrency Sentiment Analysis: A Comparative Classification Study

Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of crypto investors, known as crypto signals. This paper explores the capabilities of large language models (LLMs) and natural language processing (NLP) models in analyzing sentiment from cryptocurrency-related news articles. We fine-tune state-of-the-art models such as GPT-4, BERT, and FinBERT for this specific task, evaluating their performance and comparing their effectiveness in sentiment classification. By leveraging these advanced techniques, we aim to enhance the understanding of sentiment dynamics in the cryptocurrency market, providing insights that can inform investment decisions and risk management strategies. The outcomes of this comparative study contribute to the broader discourse on applying advanced NLP models to cryptocurrency sentiment analysis, with implications for both academic research and practical applications in financial markets.

Evaluation of a BERT Natural Language Processing Model for Automating CT and MRI Triage and Protocol Selection.

Purpose: To evaluate the accuracy of a Bidirectional Encoder Representations for Transformers (BERT) Natural Language Processing (NLP) model for automating triage and protocol selection of cross-sectional image requisitions. Methods: A retrospective study was completed using 222 392 CT and MRI studies from a single Canadian university hospital database (January 2018-September 2022). Three hundred unique protocols (116 CT and 184 MRI) were included. A BERT model was trained, validated, and tested using an 80%-10%-10% stratified split. Naive Bayes (NB) and Support Vector Machine (SVM) machine learning models were used as comparators. Models were assessed using F1 score, precision, recall, and area under the receiver operating characteristic curve (AUROC). The BERT model was also assessed for multi-class protocol suggestion and subgroups based on referral location, modality, and imaging section. Results: BERT was superior to SVM for protocol selection (F1 score: BERT-0.901 vs SVM-0.881). However, was not significantly different from SVM for triage prediction (F1 score: BERT-0.844 vs SVM-0.845). Both models outperformed NB for protocol and triage. BERT had superior performance on minority classes compared to SVM and NB. For multiclass prediction, BERT accuracy was up to 0.991 for top-5 protocol suggestion, and 0.981 for top-2 triage suggestion. Emergency department patients had the highest F1 scores for both protocol (0.957) and triage (0.986), compared to inpatients and outpatients. Conclusion: The BERT NLP model demonstrated strong performance in automating the triage and protocol selection of radiology studies, showing potential to enhance radiologist workflows. These findings suggest the feasibility of using advanced NLP models to streamline radiology operations.

BioInstruct: instruction tuning of large language models for biomedical natural language processing.

To enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its impact when combined with multi-task learning principles. We created the BioInstruct, comprising 25005 instructions to instruction-tune LLMs (LLaMA 1 and 2, 7B and 13B version). The instructions were created by prompting the GPT-4 language model with 3-seed samples randomly drawn from an 80 human curated instructions. We employed Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. We then evaluated these instruction-tuned LLMs on several BioNLP tasks, which can be grouped into 3 major categories: question answering (QA), information extraction (IE), and text generation (GEN). We also examined whether categories (eg, QA, IE, and generation) of instructions impact model performance. Comparing with LLMs without instruction-tuned, our instruction-tuned LLMs demonstrated marked performance gains: 17.3% in QA on average accuracy metric, 5.7% in IE on average F1 metric, and 96% in Generation tasks on average GPT-4 score metric. Our 7B-parameter instruction-tuned LLaMA 1 model was competitive or even surpassed other LLMs in the biomedical domain that were also fine-tuned from LLaMA 1 with vast domain-specific data or a variety of tasks. Our results also show that the performance gain is significantly higher when instruction fine-tuning is conducted with closely related tasks. Our findings align with the observations of multi-task learning, suggesting the synergies between 2 tasks. The BioInstruct dataset serves as a valuable resource and instruction tuned LLMs lead to the best performing BioNLP applications.

From text to model: Leveraging natural language processing for system dynamics model development

AbstractTextual data is abundantly available, and natural language processing (NLP) facilitates its analysis. However, system dynamics (SD) modelling relies on the modeller to identify relevant information. We explore the ability of NLP models to support SD modelling by identifying causal sentences in texts. We provide a primer on the notion of causality in SD and on the linguistic properties of causality, followed by an introduction of NLP models suitable for this task. Using three test cases, we evaluate the performance of the NLP models using common evaluation metrics and an SD model completeness metric. We conclude that NLP models can add considerable value to SD modelling, provided that remaining challenges are addressed. One such caveat is the difference we observe between information regarded as causal and information relevant for describing system structure. We discuss how these challenges can be addressed through collaboration between the NLP and SD fields. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Enhancing Clinical Documentation with Synthetic Data: Leveraging Generative Models for Improved Accuracy

Accurate and comprehensive clinical documentation is crucial for delivering high-quality healthcare, facilitating effective communication among providers, and ensuring compliance with regulatory requirements. However, manual transcription and data entry processes can be time-consuming, error-prone, and susceptible to inconsistencies, leading to incomplete or inaccurate medical records. This paper proposes a novel approach to augment clinical documentation by leveraging synthetic data generation techniques to generate realistic and diverse clinical transcripts. We present a methodology that combines state-of- the-art generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), with real-world clinical transcript and other forms of clinical data to generate synthetic transcripts. These synthetic transcripts can then be used to supplement existing documentation workflows, providing additional training data for natural language processing models and enabling more accurate and efficient transcription processes. Through extensive experiments on a large dataset of anonymized clinical transcripts, we demonstrate the effectiveness of our approach in generating high- quality synthetic transcripts that closely resemble real- world data. Quantitative evaluation metrics, including perplexity scores and BLEU scores, as well as qualitative assessments by domain experts, validate the fidelity and utility of the generated synthetic transcripts. Our findings highlight synthetic data generation's potential to address clinical documentation challenges, improving patient care, reducing administrative burdens, and enhancing healthcare system efficiency.

Natural language processing for automated breast cancer recurrence detection and classification in computed tomography reports.

e13591 Background: Relapse is a major concern for oncologists and breast cancer survivors that necessitates additional treatment and often leads to mortality. Cancer registries routinely track cancer mortality, but few monitor for relapse because of logistical challenges and prohibitive costs. In this context, Natural Language Processing (NLP) is a promising tool. Merging artificial intelligence with linguistics, NLP can rapidly analyze vast volumes of text in electronic health records. This capability of NLP is particularly valuable for Computed Tomography (CT) scans used in breast cancer care. CT scans are routinely used to characterize breast cancer progression and are described in transcribed dictations by radiologists. We aimed to apply NLP to these text reports to identify breast cancer relapses. Objective: To automate breast cancer relapse detection and classification in CT text reports using NLP. Methods: We analyzed 1,445 CT text reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. These reports underwent manual review by trained human annotators. Text was annotated to identify terminology defining local, regional, and distant breast cancer relapses. Annotated reports were partitioned into a training-validation set (90% cohort) and a test set (10% cohort) for NLP model development. Results: In our dataset of 1,445 CT text reports, 72 (5.0%) were classified as local relapse, 97 (6.7%) as regional relapse, and 743 (51.4%) as distant relapse. The performance of our NLP model using the training-validation dataset can be summarized by the following metrics and 95% confidence intervals: 94% (±3.2) accuracy for detection and 96% (±2.9) accuracy for classification. The performance of our NLP model was confirmed using the test dataset, with 90% (±4.5) accuracy for detection and 91% (±6.3) accuracy for classification. For reference, all metrics are outlined (Table). Conclusions: Our model for identifying regional and distant relapses in CT reports had excellent performance, but had lower sensitivity for local relapses, posing a risk of false negatives. Automating the identification and classification of breast cancer relapses, if used retrospectively, can enhance cancer registry data about patient outcomes and, if used prospectively, holds the potential for enhancing patient care. [Table: see text]

Population attributable fractions for risk factors for the progression of monoclonal gammopathy of undetermined significance to multiple myeloma in the Veteran population.

7552 Background: Multiple myeloma (MM) is the most common type of plasma cell cancer in the U.S. MM is consistently proceeded by monoclonal gammopathy of undetermined significance (MGUS). Studies have identified several risk factors for the progression of MGUS to MM. However, the contribution of each of these risk factors has not been quantified. We computed the population attributable fractions (PAFs) for selected risk factors in the U.S. Veterans Health Administration (VHA) health system. Methods: Veterans diagnosed with MGUS from 1999-2021 were identified and confirmed via a published natural language processing (NLP) model. We included Black and White patients whose immunoglobulin (Ig) subtype was IgA, IgG, or light chain. Patients who progressed to MM ≤6 months after MGUS diagnosis were excluded. We fit a multivariable time-to-event model controlling for sex, race (Black, White), Ig subtype (IgA, IgG, light chain), agent orange (AO) exposure, as well as M-protein level (≤, >1.5 g/dL), Charlson Comorbidity Index (CCI), obesity status (underweight, normal weight, overweight, obese), and age, all at MGUS diagnosis. The outcome was progression of MGUS to MM, which was also confirmed by a published NLP model. PAF for a risk factor is the fraction of all MM cases in the veteran population that is attributable to this specific risk factor. It accounts for both the prevalence and relative risk of this factor in the population. We calculated PAF for black race, IgA, AO exposure, and overweight/obesity. Results: The analysis included 24,917 patients with MGUS. Among them, 7.8% (n=1,944) progressed to MM (follow-up median 5.6, IQR 3.3-8.8 years). In the veteran population with MGUS, 17.9% of all MM cases was attributable to overweight or obesity (PAF 17.9%, 95% confidence interval [CI] 11.0-24.3%); 7.6% was attributable to black race (PAF 7.6%, 95% CI 4.3-10.9%), 5.7% was attributable to IgA (PAF 5.7%, 95% CI 4.0-7.4%), and 2.4% was attributable to AO exposure (PAF 2.4%, 95% CI 1.0-3.9%). Conclusions: In the veteran population with MGUS, overweight/obesity, the only modifiable factor, is the top contributor to progression to MM cases. In veterans with MGUS, had the overweight or obese MGUS patients to be reversed to normal weight, an estimated 17.9% of the progression cases could have been avoided.Our findings highlight the importance of maintaining a normal weight for reducing the risk of progression of MGUS to MM. [Table: see text]

Evaluating the machine learning models based on natural language processing tasks

<span lang="EN-US">In the realm of natural language processing (NLP), a diverse array of language models has emerged, catering to a wide spectrum of tasks, ranging from speaker recognition and auto-correction to sentiment analysis and stock prediction. The significance of language models in enabling the execution of these NLP tasks cannot be overstated. This study proposes an approach to enhance accuracy by leveraging a hybrid language model, combining the strengths of long short-term memory (LSTM) and gated recurrent unit (GRU). LSTM excels in preserving long-term dependencies in data, while GRU's simpler gating mechanism expedites the training process. The research endeavors to evaluate four variations of this hybrid model: LSTM, GRU, <a name="_Hlk158992630"></a>bidirectional long short-term memory (Bi-LSTM), and a combination of LSTM with GRU. These models are subjected to rigorous testing on two distinct datasets: one focused on IBM stock price prediction, and the other on Jigsaw toxic comment classification (sentiment analysis). This work represents a significant stride towards democratizing NLP capabilities, ensuring that even in resource-constrained settings, NLP models can exhibit improved performance. The anticipated implications of these findings span a wide spectrum of real-world applications and hold the potential to stimulate further research in the field of NLP. </span>

Shakespeare Machine: New AI-Based Technologies for Textual Analysis

Abstract This article demonstrates a method using tools from the field of Natural Language Processing (NLP) to aid in analyzing theatrical texts and similar works. The method deploys pre-trained large language model neural networks to gather metadata for a text that is amenable to downstream statistical analyses surfacing patterns of interest in character dialogue. We specifically focus on Shakespeare’s works, collecting metadata in the form of sentiment and emotion scores for each line of his plays. In addition to sentiment and emotion scores produced by NLP models, we also directly gather metadata such as genre, line length, and character gender. We show how these metadata may be used to illuminate a number of interesting patterns in Shakespearean character which may be difficult to detect from a direct reading of the texts. We use these metadata to expose statistically significant relationships in Shakespeare between character gender and the emotional content of that character’s dialogue, controlling for genre. We also present here the publicly available dataset that we have compiled to perform these analyses. The data collects text from Shakespeare’s plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare’s works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models.