Natural Language Research Articles

The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.

The BioRED track at BioCreative VIII calls for a community effort to identify, semantically categorize, and highlight the novelty factor of the relationships between biomedical entities in unstructured text. Relation extraction is crucial for many biomedical natural language processing (NLP) applications, from drug discovery to custom medical solutions. The BioRED track simulates a real-world application of biomedical relationship extraction, and as such, considers multiple biomedical entity types, normalized to their specific corresponding database identifiers, as well as defines relationships between them in the documents. The challenge consisted of two subtasks: (i) in Subtask 1, participants were given the article text and human expert annotated entities, and were asked to extract the relation pairs, identify their semantic type and the novelty factor, and (ii) in Subtask 2, participants were given only the article text, and were asked to build an end-to-end system that could identify and categorize the relationships and their novelty. We received a total of 94 submissions from 14 teams worldwide. The highest F-score performances achieved for the Subtask 1 were: 77.17% for relation pair identification, 58.95% for relation type identification, 59.22% for novelty identification, and 44.55% when evaluating all of the above aspects of the comprehensive relation extraction. The highest F-score performances achieved for the Subtask 2 were: 55.84% for relation pair, 43.03% for relation type, 42.74% for novelty, and 32.75% for comprehensive relation extraction. The entire BioRED track dataset and other challenge materials are available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/ and https://codalab.lisn.upsaclay.fr/competitions/13377 and https://codalab.lisn.upsaclay.fr/competitions/13378. Database URL: https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/https://codalab.lisn.upsaclay.fr/competitions/13377https://codalab.lisn.upsaclay.fr/competitions/13378.

Natural Language Processing System for Text Classification Corpus Based on Machine Learning

A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation safety management system is selected by dead bodies, classified and marked in five levels. Time Frame Return Frequency TextRank text classification method based on key content extraction and text classification model based on Convolutional Neural Network and Bidirectional Encoder Representations from Transforms models were used in the experiment to solve the problem of small samples, many labels and random samples in hazardous environment of air pollution control. The results show that the total cost of model training time and classification accuracy is the highest when the keywords are around 8. As the number of points increases, the time spent in dimensioning decreases and affects accuracy. When the number of points reaches about 93, the time spent in determining the size increases, but the accuracy of the allocation remains close to 0.7, but the increase in the value of time leads to a decrease in the total cost. It has been proven that extracting key content can solve text classification problems for small companies and contribute to further research in the development of security systems.

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

Engagement With Conversational Agent-Enabled Interventions in Cardiometabolic Disease Management: Protocol for a Systematic Review.

Cardiometabolic diseases (CMDs) are a group of interrelated conditions, including heart failure and diabetes, that increase the risk of cardiovascular and metabolic complications. The rising number of Australians with CMDs has necessitated new strategies for those managing these conditions, such as digital health interventions. The effectiveness of digital health interventions in supporting people with CMDs is dependent on the extent to which users engage with the tools. Augmenting digital health interventions with conversational agents, technologies that interact with people using natural language, may enhance engagement because of their human-like attributes. To date, no systematic review has compiled evidence on how design features influence the engagement of conversational agent-enabled interventions supporting people with CMDs. This review seeks to address this gap, thereby guiding developers in creating more engaging and effective tools for CMD management. The aim of this systematic review is to synthesize evidence pertaining to conversational agent-enabled intervention design features and their impacts on the engagement of people managing CMD. The review is conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions and reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Searches will be conducted in the Ovid (Medline), Web of Science, and Scopus databases, which will be run again prior to manuscript submission. Inclusion criteria will consist of primary research studies reporting on conversational agent-enabled interventions, including measures of engagement, in adults with CMD. Data extraction will seek to capture the perspectives of people with CMD on the use of conversational agent-enabled interventions. Joanna Briggs Institute critical appraisal tools will be used to evaluate the overall quality of evidence collected. This review was initiated in May 2023 and was registered with the International Prospective Register of Systematic Reviews (PROSPERO) in June 2023, prior to title and abstract screening. Full-text screening of articles was completed in July 2023 and data extraction began August 2023. Final searches were conducted in April 2024 prior to finalizing the review and the manuscript was submitted for peer review in July 2024. This review will synthesize diverse observations pertaining to conversational agent-enabled intervention design features and their impacts on engagement among people with CMDs. These observations can be used to guide the development of more engaging conversational agent-enabled interventions, thereby increasing the likelihood of regular intervention use and improved CMD health outcomes. Additionally, this review will identify gaps in the literature in terms of how engagement is reported, thereby highlighting areas for future exploration and supporting researchers in advancing the understanding of conversational agent-enabled interventions. PROSPERO CRD42023431579; https://tinyurl.com/55cxkm26. DERR1-10.2196/52973.

The Case for a Quantitative Approach to the Study of Nonnative Accent Features.

Research with nonnative speech spans many different linguistic branches and topics. Most studies include one or a few well-known features of a particular accent. However, due to a lack of empirical studies, little is known about how common these features are among nonnative speakers or how uncommon they are among native speakers. Moreover, it remains to be seen whether findings from such studies generalize to lesser-known features. Here, we demonstrate a quantitative approach to study nonnative accent features using Dutch-accented English as an example. By analyzing the phonetic distances between transcriptions of speech samples, this approach can identify the features that best distinguish nonnative from native speech. In addition, we describe a method to test hypotheses about accent features by checking whether the prevalence of the features overall varies between native and nonnative speakers. Furthermore, we include English speakers from the United States and United Kingdom and native Dutch speakers from Belgium and The Netherlands to address the issue of regional accent variability in both the native and target language. We discuss the results concerning three observed features. Overall, the results provide empirical support for some well-known features of Dutch-accented English, but suggest that others may be infrequent among nonnatives or in fact frequent among natives. In addition, the findings reveal potentially new accent features, and factors that may modulate the expression of known features. Our study demonstrates a fruitful approach to study nonnative accent features that has the potential to expand our understanding of the phenomenon of accent.

Beyond the border: comparative ethnobotany in Valmalenco (SO, Italy) and Valposchiavo (Canton of Grisons, Switzerland)

BackgroundThe ethnobotanical analysis of two bordering areas allows for the in-depth understanding of the dynamics of Local Ecological Knowledge, which mirrors the naturalistic, historical, and sociopolitical features of each area. As part of the Interreg Italy-Switzerland B-ICE&Heritage and GEMME projects, this work is an ethnobotanical comparative study of two neighboring Alpine territories: Valmalenco (Italy) and Valposchiavo (Switzerland).MethodsA total of 471 informants were interviewed on different fields of use (medicinal, food, veterinary, etc.). All data were organized in Excel™ spreadsheets. Informant Consensus Factor was calculated for the pathologies reported. Jaccard’s similarity indices were calculated to compare the Valmalenco and Valposchiavo areas. Subsequently, another comparison between Valmalenco/Valposchiavo and Italian/Swiss Alpine neighboring areas was carried out.ResultsThe number of taxa for Valmalenco was 227 (77 families) and 226 in Valposchiavo (65). Out of the 10 most cited species, 7 were mentioned in both. Arnica montana L. was the most cited in Valmalenco, and Sambucus nigra L. in Valposchiavo. The 5 most cited families were the same. Regarding the medicinal and food fields, the similarity indices were fairly low (0.31 and 0.34 for the species; 0.22 and 0.31 for the uses). Concerning the comparison with Italian and Swiss Alps, similarity values were slightly higher with Italy (Valmalenco food species: 0.38 with Italy and 0.26 with Switzerland, medicinal: 0.26 IT and 0.14 SW; Valposchiavo food species: 0.36 with IT and 0.26 with SW, medicinal: 0.21 IT and 0.14 SW).ConclusionAlthough Valmalenco and Valposchiavo partly share natural environment, language, history, and culture, they had low similarity indices. They both seemed to have more similarities with Italy than Switzerland, maintaining low values with the surrounding territories too. They showed a common core of Local Ecological Knowledge with several divergent branches possibly due to pivotal historical happenings, as well as more modern external influences.

Text Classification Based on Natural Language Processing and Machine Learning in Multi-Label Corpus

The rapid development of the Internet has led to a geometric expansion of text information resources online. Among them, corpus, as the basic data source of natural language processing based on statistical language model, its construction and application have become a hot issue in current language processing research. After consulting a large number of relevant literature and materials, it was found that many researchers have provided new ideas for multi label corpus text classification methods. However, this article adds its own understanding and takes this as the direction and basis. In the introduction, the research significance of text classification was introduced, and then academic research and analysis were carried out on the two key sentences of corpus text classification and natural language processing in multi-tag corpus text classification. This article then utilizes an algorithm model to provide a theoretical basis for the study of multi-label corpus text classification methods; At the end of this article, a simulation comparative experiment is conducted, and the experiment is summarized and discussed; In the Enterprise L corpus, the difference in recall rates before and after the use of Entrance 1 was 5.5%, the difference in recall rates before and after the use of Entrance 2 was 7.8%, the difference in recall rates before and after the use of Entrance 3 was 3.3%, and the difference in recall rates before and after the use of Entrance 4 was 4.5%. At the same time, with the continuous research of natural language processing and machine learning, the research on text classification methods of multi tag corpus is also facing new opportunities and challenges.

A transformer-based spelling error correction framework for Bangla and resource scarce Indic languages

Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is a potential and active research topic in Natural Language Processing because of numerous applications in human language understanding. The phonetically or visually similar yet semantically distinct characters make it an arduous task in any language. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient. In particular, machine learning-based approaches, which exhibit superior performance to rule-based and statistical methods, are ineffective as they correct each character regardless of its appropriateness. In this paper, we propose a novel detector-purificator-corrector framework, DPCSpell based on denoising transformers by addressing previous issues. In addition to that, we present a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language. The empirical outcomes demonstrate the effectiveness of our approach, which outperforms previous state-of-the-art methods by attaining an exact match (EM) score of 94.78%, a precision score of 0.9487, a recall score of 0.9478, an f1 score of 0.948, an f0.5 score of 0.9483, and a modified accuracy (MA) score of 95.16% for Bangla spelling error correction. The models and corpus are publicly available at https://tinyurl.com/DPCSpell.

DialogueNeRF: towards realistic avatar face-to-face conversation video generation

Conversation is an essential component of virtual avatar activities in the metaverse. With the development of natural language processing, significant breakthroughs have been made in text and voice conversation generation. However, face-to-face conversations account for the vast majority of daily conversations, while most existing methods focused on single-person talking head generation. In this work, we take a step further and consider generating realistic face-to-face conversation videos. Conversation generation is more challenging than single-person talking head generation, because it requires not only the generation of photo-realistic individual talking heads, but also the listener’s response to the speaker. In this paper, we propose a novel unified framework based on the neural radiance field (NeRF) to address these challenges. Specifically, we model both the speaker and the listener with a NeRF framework under different conditions to control individual expressions. The speaker is driven by the audio signal, while the response of the listener depends on both visual and acoustic information. In this way, face-to-face conversation videos are generated between human avatars, with all the interlocutors modeled within the same network. Moreover, to facilitate future research on this task, we also collected a new human conversation dataset containing 34 video clips. Quantitative and qualitative experiments evaluate our method in different aspects, e.g., image quality, pose sequence trend, and natural rendering of the scene in the generated videos. Experimental results demonstrate that the avatars in the resulting videos are able to carry on a realistic conversation, and maintain individual styles.

Does gender impact the relationship between perceived value and intentions of use of natural processing models?

Purpose In the era of artificial intelligence, natural language processing (NLP) models are revolutionizing numerous sectors. This research aims to explore the perceived value of them among university students. In particular, it aims to investigate how gender may influence students’ intention to use these models in educational contexts, highlighting potentially significant differences that could inform the implementation and adoption of educational technologies. Design/methodology/approach This study investigates the relationship between perceived value and students' intention to adopt NLP models, considering gender as a moderator. The research involves 562 students from the University of Salerno, in Italy, and uses confirmatory factor analysis to evaluate the reliability and validity of the measurement scales. A regression model with robust errors is used to explore the moderating role of gender on the relationship between perceived value and intentions of use of NLP models. Findings The results reveal a significant positive association between perceived value and intention to use NLP models, confirming that students with higher perceived value are more likely to adopt these technologies. Furthermore, gender moderates this relationship, indicating that females are less prone to use NLP models than male counterparts. Originality/value Research takes on a significant role in the academic field, underlining the importance of adapting teaching practices to the increasingly widespread digitalization. The inclusion of NLP models in university programs emerges as a possible improvement of the learning experience, ensuring cutting-edge education in tune with the needs of the digital society.

Enhancing Asian Indigenous Language Processing through Deep Learning-based Handwriting Recognition and Optimization Techniques

Asian indigenous language or autochthonous language is a language which is native to a region and spoken by indigenous people in Asia. This language is a linguistically different community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages have attained important internet amongst the research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low-resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is the most ancient Indian language that is mostly exploited in the Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning-based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in the Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs a capsule network (CapsNet) model for feature vector generation with the HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.

Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning

The medical security privacy and named entity recognition (NER) technology under the blockchain technology has been a hot topic in all walks of life. As a typical representative of medical security risks and NER, the NER model of online social media based on active learning has attracted worldwide attention. NER is an important part of natural language processing. Traditional recognition technology usually requires a lot of external information, and through the manual identification of its features, which costs a lot of time and energy. In order to solve the shortcomings of traditional recognition algorithms and the lack of feature extraction in network media NER, a new active learning model was introduced in this paper. In the information age, people are increasingly demanding a large amount of text information, and NER technology came into being. Its main function is to accurately identify important information from text and provide useful information for high-level work. The initial design of NER system is mainly based on the recognition of rules, so as to realize the recognition of named entities. However, in a complex network environment, it takes a lot of time and energy to establish rules without conflicts, and it has poor mobility. In recent years, with the continuous development of computer technology, the use of machine learning to actively learn the unknown information in the target area reduces the workload of manual annotation, thus realizing the active learning of large amounts of data. The research showed that the recognition accuracy under the traditional NER was low, and the information processing speed was slow; the accuracy rate of NER based on active learning was as high as 97%, and the speed of information processing had also been greatly improved, which had solved many problems under the traditional mode. User satisfaction could be as high as 95%, which showed that the latter had broad prospects. The progress of the new era cannot be separated from the support of new technologies. The research of this article has important guiding significance for medical security privacy and the application of NER under blockchain technology.

Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models.

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

Enhancing Coherence and Diversity in Multi-class Slogan Generation Systems

Many problems related to natural language processing are solved by neural networks and big data. Researchers have previously focused on single-task supervised goals with limited data management to train slogan classification. A multi-task learning framework is used to learn jointly across several tasks related to generating multi-class slogan types. This study proposes a multi-task model named slogan generative adversarial network systems (Slo-GAN) to enhance coherence and diversity in slogan generation, utilizing generative adversarial networks and recurrent neural networks (RNN). Slo-GAN generates a new text slogan-type corpus, and the training generalization process is improved. We explored active learning (AL) and meta-learning (ML) for dataset labeling efficiency. AL reduced annotations by 10% compared to ML but still needed about 70% of the full dataset for baseline performance. The whole framework of Slo-GAN is supervised and trained together on all of these tasks. The text with the higher reporting score level is filtered by Slo-GAN, and a classification accuracy of 87.2% is achieved. We leveraged relevant datasets to perform a cross-domain experiment, reinforcing our assertions regarding both the distinctiveness of our dataset and the challenges of adapting bilingual dialects to one another.

Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Trustworthy Response Generation in Chinese

Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the hallucination about medical facts due to limited domain knowledge. Such shortcomings pose potential risks in the utilization of LLMs within medical contexts. To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate trustworthy response generation. We also release cMedKnowQA, a Chinese medical knowledge question-answering dataset constructed from medical knowledge bases to assess the medical knowledge proficiency of LLMs. Experimental results show that the LLMs which are knowledge-tuned with cMedKnowQA, can exhibit higher levels of accuracy in response generation compared with vanilla instruction-tuning and offer a new trustworthy way for the domain adaptation of LLMs. We release our code and data at https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese .

Disease outbreak prediction using natural language processing: a review

Disease outbreak prediction using natural language processing: a review

An effective multi-modal adaptive contextual feature information fusion method for Chinese long text classification

Chinese long text classification plays a vital role in Natural Language Processing. Compared to Chinese short texts, Chinese long texts contain more complex semantic feature information. Furthermore, the distribution of these semantic features is uneven due to the varying lengths of the texts. Current research on Chinese long text classification models primarily focuses on enhancing text semantic features and representing Chinese long texts as graph-structured data. Nonetheless, these methods are still susceptible to noise information and tend to overlook the deep semantic information in long texts. To address the above challenges, this study proposes a novel and effective method called MACFM, which introduces a deep feature information mining method and an adaptive modal feature information fusion strategy to learn the semantic features of Chinese long texts thoroughly. First, we present the DCAM module to capture complex semantic features in Chinese long texts, allowing the model to learn detailed high-level representation features. Then, we explore the relationships between word vectors and text graphs, enabling the model to capture abundant semantic information and text positional information from the graph. Finally, we develop the AMFM module to effectively combine different modal feature representations and eliminate the unrelated noise information. The experimental results on five Chinese long text datasets show that our method significantly improves the accuracy of Chinese long text classification tasks. Furthermore, the generalization experiments on five English datasets and the visualized results demonstrate the effectiveness and interpretability of the MACFM model.

Too much focus on your health might be bad for your health: Reddit user's communication style predicts their Long COVID likelihood.

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective.

Stylometric Analysis of AI Chatbot-Generated Emails: Are Students Losing Their Linguistic Fingerprint?

The integration of artificial intelligence (AI) in education, particularly through AI chatbots like ChatGPT, Copilot, Gemini, and Bing, has revolutionized second language teaching and learning. These chatbots, which utilize advanced natural language processing and machine learning algorithms, hold great potential. They can operate complex cognitive tasks, provide immediate feedback, enhance motivation and self-confidence, reduce anxiety and improve language skills. Despite these advantages, concerns about academic integrity, loss of unique voice, and lack of emotional depth have emerged. This research aims at conducting a stylometric analysis on 25 AI chatbot-generated emails produced by undergraduate English language learners at Sultan Qaboos University. By examining stylistic features like tone, lexical density, lexical diversity, choice of words, repetition, formality level and emotional depth, the study will provide insights into the strengths and limitations of AI chatbots in enhancing email writing skills while preserving the unique voice of student writing. Findings reveal that AI-generated emails are characterized by a repetitive structure, a high level of formality and politeness, a high lexical diversity and a lack of emotional depth and personal anecdotes. The outcomes of this research will shed light on the awareness and skills students need to acquire to optimize the use of AI chatbots without losing their linguistic fingerprint.

Diagnostic Discordance, Uncertainty, and Treatment Ambiguity in Community-Acquired Pneumonia : A National Cohort Study of 115 U.S. Veterans Affairs Hospitals.

Evidence-based practice in community-acquired pneumonia often assumes an accurate initial diagnosis. To examine the evolution of pneumonia diagnoses among patients hospitalized from the emergency department (ED). Retrospective nationwide cohort. 118 U.S. Veterans Affairs medical centers. Aged 18 years or older and hospitalized from the ED between 1 January 2015 and 31 January 2022. Discordances between initial pneumonia diagnosis, discharge diagnosis, and radiographic diagnosis identified by natural language processing of clinician text, diagnostic coding, and antimicrobial treatment. Expressions of uncertainty in clinical notes, patient illness severity, treatments, and outcomes were compared. Among 2 383 899 hospitalizations, 13.3% received an initial or discharge diagnosis and treatment of pneumonia: 9.1% received an initial diagnosis and 10.0% received a discharge diagnosis. Discordances between initial and discharge occurred in 57%. Among patients discharged with a pneumonia diagnosis and positive initial chest image, 33% lacked an initial diagnosis. Among patients diagnosed initially, 36% lacked a discharge diagnosis and 21% lacked positive initial chest imaging. Uncertainty was frequently expressed in clinical notes (58% in ED; 48% at discharge); 27% received diuretics, 36% received corticosteroids, and 10% received antibiotics, corticosteroids, and diuretics within 24 hours. Patients with discordant diagnoses had greater uncertainty and received more additional treatments, but only patients lacking an initial pneumonia diagnosis had higher 30-day mortality than concordant patients (14.4% [95% CI, 14.1% to 14.7%] vs. 10.6% [CI, 10.4% to 10.7%]). Patients with diagnostic discordance were more likely to present to high-complexity facilities with high ED patient load and inpatient census. Retrospective analysis; did not examine causal relationships. More than half of all patients hospitalized and treated for pneumonia had discordant diagnoses from initial presentation to discharge. Treatments for other diagnoses and expressions of uncertainty were common. These findings highlight the need to recognize diagnostic uncertainty and treatment ambiguity in research and practice of pneumonia-related care. The Gordon and Betty Moore Foundation.