Natural Language Processing Tools Research Articles

An Analysis of Journalists Coping Strategies in the Face of ChatGPT

Artificial intelligence technology powers ChatGPT, a tool for natural language processing. On the one hand, this kind of more intelligent and cutting-edge technology development has promoted the continuous change of the industrial structure of journalism; on the other hand, it has also caused a large shock to journalists. How journalists use ChatGPT correctly is the research topic of this paper. This paper uses the literature research method and the case study method to focus on the relevant concepts and connotations of ChatGPT, conduct specific research on the application of ChatGPT in journalism, and further analyse the double-sided impact of ChatGPT on journalism. Finally, the paper discusses the coping strategies of journalists in the context of the application of ChatGPT technology at two different levels. In the short term, journalists should learn to master the new artificial intelligence technology of ChatGPT. In the long term, the development prospects for journalists are accompanied by threats. Human beings need to conform to the trend of the times and improve their news capabilities and levels.

Improving the Performance of Arabic Information Retrieval Systems: The Issue of Resolving Word Sense Disambiguation

This study aimed at assessing the performance and efficacy of the retrieval information (IR) systems implemented in three widely used search engines (Google, Bing, and Yahoo), specifically with regard to the challenge of word sense disambiguation in Arabic texts. Such a challenge has been confirmed to negatively influence the retrieval of the most relevant documents. Therefore, we extended the paradigm of using computational methods and natural language processing (NLP) tools, primarily tailored for processing English texts, to explore morphosyntactic as well as lexical issues disturbing the accuracy of Arabic IR systems. Findings revealed striking disparities in the efficacy of IR systems integrated into these search engines, which can be attributed to four principal challenges: (a) the intricate morpho-syntactic structures inherent in Arabic; (b) the idiosyncratic orthographical system of the Arabic script; (c) the multifaceted semantic flexibility of certain lexical elements; and (d) the intriguing diaglossic nature of Arabic, allowing for the coexistence of multiple linguistic varieties within a single discourse situation. Drawing from these findings, a series of solutions rooted in supervised machine learning techniques, including clustering models and adaptations based on geographic locations, are proposed. Moreover, the study advocates for the capacity of search engines to interpret queries across all Arabic varieties, encompassing vernacular dialects. Furthermore, the importance of search engines accommodating queries irrespective of the specific language adopted by users is underscored. While the research primarily centers on Arabic, its implications resonate beyond this language alone. By applying computational methodologies originally designed for English to Arabic, the study not only addresses the challenges specific to Arabic IR systems but also contributes valuable insights that transcend linguistic boundaries. Through a comparative lens, issues like word sense disambiguation between Arabic and English are juxtaposed, extracting lessons that can inform advancements in information retrieval for both languages.

Exploring large language model for next generation of artificial intelligence in ophthalmology

In recent years, ophthalmology has advanced significantly, thanks to rapid progress in artificial intelligence (AI) technologies. Large language models (LLMs) like ChatGPT have emerged as powerful tools for natural language processing. This paper finally includes 108 studies, and explores LLMs’ potential in the next generation of AI in ophthalmology. The results encompass a diverse range of studies in the field of ophthalmology, highlighting the versatile applications of LLMs. Subfields encompass general ophthalmology, retinal diseases, anterior segment diseases, glaucoma, and ophthalmic plastics. Results show LLMs’ competence in generating informative and contextually relevant responses, potentially reducing diagnostic errors and improving patient outcomes. Overall, this study highlights LLMs’ promising role in shaping AI’s future in ophthalmology. By leveraging AI, ophthalmologists can access a wealth of information, enhance diagnostic accuracy, and provide better patient care. Despite challenges, continued AI advancements and ongoing research will pave the way for the next generation of AI-assisted ophthalmic practices.

Making Use of Natural Language Processing to Better Understand Medical Students' Self-Assessment of Clinical Skills.

Reflective practice is necessary for self-regulated learning. Helping medical students develop these skills can be challenging since they are difficult to observe. One common solution is to assign students' reflective self-assessments, which produce large quantities of narrative assessment data. Reflective self-assessments also provide feedback to faculty regarding students' understanding of content, reflective abilities, and areas for course improvement. To maximize student learning and feedback to faculty, reflective self-assessments must be reviewed and analyzed, activities that are often difficult for faculty due to the time-intensive and cumbersome nature of processing large quantities of narrative assessment data. The authors collected narrative assessment data (2,224 students' reflective self-assessments) from 344 medical students' reflective self-assessments. In academic years 2019-2020 and 2021-2022, students at the University of Cincinnati College of Medicine responded to 2 prompts (aspects that surprised students, areas for student improvement) after reviewing their standardized patient encounters. These free-text entries were analyzed using TopEx, an open-source natural language processing (NLP) tool, to identify common topics and themes, which faculty then reviewed. TopEx expedited theme identification in students' reflective self-assessments, unveiling 10 themes for prompt 1 such as question organization and history analysis, and 8 for prompt 2, including sensitive histories and exam efficiency. Using TopEx offered a user-friendly, time-saving analysis method without requiring complex NLP implementations. The authors discerned 4 education enhancement implications: aggregating themes for future student reflection, revising self-assessments for common improvement areas, adjusting curriculum to guide students better, and aiding faculty in providing targeted upcoming feedback. The University of Cincinnati College of Medicine aims to refine and expand the utilization of TopEx for deeper narrative assessment analysis, while other institutions may model or extend this approach to uncover broader educational insights and drive curricular advancements.

ChatGPT API: Brief overview and integration in Software Development

Powerful natural language processing tools have been developed and since the end of 2022 and so far in 2023 ChatGPT has become the most successful, in this sense this paper provides an overview of the ChatGPT API, addresses how this API can be used effectively in the field of software development, highlighting its key applications and advantages. The results highlight the potential of the ChatGPT API as a valuable tool in software development, offering intelligent and efficient solutions for a variety of applications. It emphasizes the importance of understanding how to integrate and leverage this API effectively, and concludes that its proper implementation can significantly improve the quality and efficiency of software development projects.

Robust Multi-View Fracture Detection inthe Presence ofOther Abnormalities Using HAMIL-Net.

Foot and ankle fractures are the most common military health problem. Automated diagnosis can save time and personnel. It is crucial to distinguish fractures not only from normal healthy cases, but also robust against the presence of other orthopedic pathologies. Artificial intelligence (AI) deep learning has been shown to be promising. Previously, we have developed HAMIL-Net to automatically detect orthopedic injuries for upper extremity injuries. In this research, we investigated the performance of HAMIL-Net for detecting foot and ankle fractures in the presence of other abnormalities. HAMIL-Net is a novel deep neural network consisting of a hierarchical attention layer followed by a multiple-instance learning layer. The design allowed it to deal with imaging studies with multiple views. We used 148K musculoskeletal imaging studies for 51K Veterans at VA San Diego in the past 20 years to create datasets for this research. We annotated each study by a semi-automated pipeline leveraging radiology reports written by board-certified radiologists and extracting findings with a natural language processing tool and manually validated the annotations. HAMIL-Net can be trained with study-level, multiple-view examples, and detect foot and ankle fractures with a 0.87 area under the receiver operational curve, but the performance dropped when tested by cases including other abnormalities. By integrating a fracture specialized model with one that detecting a broad range of abnormalities, HAMIL-Net's accuracy of detecting any abnormality improved from 0.53 to 0.77 and F-score from 0.46 to 0.86. We also reported HAMIL-Net's performance under different study types including for young (age 18-35) patients. Automated fracture detection is promising but to be deployed in clinical use, presence of other abnormalities must be considered to deliver its full benefit. Our results with HAMIL-Net showed that considering other abnormalities improved fracture detection and allowed for incidental findings of other musculoskeletal abnormalities pertinent or superimposed on fractures.

Abstract 14123: Cardiovascular Disease Incidence Among Female Veterans by Sexual Orientation and Race

Introduction: Studies of cardiovascular disease (CVD) in sexual minority (SM) women (i.e., lesbian, bisexual) vs. heterosexual women are limited by small sample sizes and lack of objective CVD ascertainment. Small samples also hinder study at the intersection of race and sexual orientation on CVD events. We used the Veterans Healthcare Administration Lesbian, Gay, Bisexual, Transgender Electronic Health Record cohort to fill these knowledge gaps. Methods: We studied 132,571 female cisgender veterans who were free of prevalent CVD and classified as SM or non-SM by a natural language processing tool. We defined incident CVD as first instance of AMI, ischemic stroke, cardiac revascularization, or CVD death between baseline and date of death or 8/31/22. We stratified sample statistics by SM status and race. We used Cox proportional hazards regression to assess the intersection of sexual orientation and race with incident CVD; we adjusted for Hispanic ethnicity and age. Based on the minority stress model, non-SM White women were the referent. Results: Within races, traditional CVD risk factors were similarly distributed by SM status but SM women had greater prevalence of stress-associated characteristics like substance use, depression, and anxiety. Regardless of sexual orientation, Asian women had lower CVD risk than non-SM White women, while Black/African American women had persistently increased CVD risk ( Table ). White lesbians had increased CVD risk vs. their non-SM counterparts. Unclassified SM women of American Native race or multiple races also had increased CVD incidence vs. non-SM White women. Conclusion: This is one of the first studies to examine the intersection of sexual orientation and race on CVD risk in female veterans. CVD risk appears increased or decreased in certain racial groups regardless of sexual orientation but this is not always true and invites further study into the mechanisms by which stress results in inequitable CVD events across groups.

OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis.

By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL https://oncoctminer.chosenmedinfo.com.

Intimate partner violence and suicide mortality: a cross-sectional study using machine learning and natural language processing of suicide data from 43 states

IntroductionIntimate partner violence (IPV) is associated with suicidal ideation, yet it remains unclear how often IPV precipitates suicide mortality. To overcome limitations with national data, we applied novel methods to:...

Low-Resource Language Processing Using Improved Deep Learning with Hunter–Prey Optimization Algorithm

Low-resource language (LRL) processing refers to the development of natural language processing (NLP) techniques and tools for languages with limited linguistic resources and data. These languages often lack well-annotated datasets and pre-training methods, making traditional approaches less effective. Sentiment analysis (SA), which involves identifying the emotional tone or sentiment expressed in text, poses unique challenges for LRLs due to the scarcity of labelled sentiment data and linguistic intricacies. NLP tasks like SA, powered by machine learning (ML) techniques, can generalize effectively when trained on suitable datasets. Recent advancements in computational power and parallelized graphical processing units have significantly increased the popularity of deep learning (DL) approaches built on artificial neural network (ANN) architectures. With this in mind, this manuscript describes the design of an LRL Processing technique that makes use of Improved Deep Learning with Hunter–Prey Optimization (LRLP-IDLHPO). The LRLP-IDLHPO technique enables the detection and classification of different kinds of sentiments present in LRL data. To accomplish this, the presented LRLP-IDLHPO technique initially pre-processes these data to improve their usability. Subsequently, the LRLP-IDLHPO approach applies the SentiBERT approach for word embedding purposes. For the sentiment classification process, the Element-Wise–Attention GRU network (EWAG-GRU) algorithm is used, which is an enhanced version of the recurrent neural network. The EWAG-GRU model is capable of processing temporal features and includes an attention strategy. Finally, the performance of the EWAG-GRU model can be boosted by adding the HPO algorithm for use in the hyperparameter tuning process. A widespread simulation analysis was performed to validate the superior results derived from using the LRLP-IDLHPO approach. The extensive results indicate the significant superiority of the performance of the LRLP-IDLHPO technique compared to the state-of-the-art approaches described in the literature.

An open-source natural language processing toolkit to support software development: addressing automatic bug detection, code summarisation and code search.

This paper aims to introduce the innovative work carried out in the Horizon 2020 DECODER project - acronym for "DEveloper COmpanion for Documented and annotatEd code Reference" - (Grant Agreement no. 824231) by linking the fields of natural language processing (NLP) and software engineering. The project as a whole addresses the development of a framework, namely the Persistent Knowledge Monitor (PKM), that acts as a central infrastructure to store, access, and trace all the data, information and knowledge related to a given software or ecosystem. This meta-model defines the knowledge base that can be queried and analysed by all the tools integrated and developed in DECODER. Besides, the DECODER project offers a friendly user interface where each of the predefined three roles (i.e., developers, maintainers and reviewers) can access and query the PKM with their personal accounts. The paper focuses on the NLP tools developed and integrated in the PKM, namely the deep learning models developed to perform variable misuse, code summarisation and semantic parsing. These were developed under a common work package - "Activities for the developer" - intended to precisely target developers, who can perform tasks such as detection of bugs, automatic generation of documentation for source code and generation of code snippets from natural languages instructions, among the multiple functionalities that DECODER offers. These tools assist and help the developers in the daily work, by increasing their productivity and avoiding loss of time in tedious tasks such as manual bug detection. Training and validation were conducted for four use cases in Java, C and C++ programming languages in order to evaluate the performance, suitability, usability, etc. of the developed tools.

Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis.

Past research in the Western context found that people with dementia search for digital dementia information in peer-reviewed medical research articles, dementia advocacy and medical organizations, and blogs written by other people with dementia. This past work also demonstrated that people with dementia do not perceive English digital dementia information as emotionally or cognitively accessible. In this study, we sought to investigate the readability; linguistic, psychological, and emotional characteristics; and target audiences of digital dementia information. We conducted a textual analysis of 3 different types of text-based digital dementia information written in English: 300 medical articles, 35 websites, and 50 blogs. We assessed the text's readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level measurements, as well as tone, analytical thinking, clout, authenticity, and word frequencies using a natural language processing tool, Linguistic Inquiry and Word Count Generator. We also conducted a thematic analysis to categorize the target audiences for each information source and used these categorizations for further statistical analysis. The median Flesch-Kincaid Grade Level readability score and Flesch Reading Ease score for all types of information (N=1139) were 12.1 and 38.6, respectively, revealing that the readability scores of all 3 information types were higher than the minimum requirement. We found that medical articles had significantly (P=.05) higher word count and analytical thinking scores as well as significantly lower clout, authenticity, and emotional tone scores than websites and blogs. Further, blogs had significantly (P=.48) higher word count and authenticity scores but lower analytical scores than websites. Using thematic analysis, we found that most of the blogs (156/227, 68.7%) and web pages (399/612, 65.2%) were targeted at people with dementia. Website information targeted at a general audience had significantly lower readability scores. In addition, website information targeted at people with dementia had higher word count and lower emotional tone ratings. The information on websites targeted at caregivers had significantly higher clout and lower authenticity scores. Our findings indicate that there is an abundance of digital dementia information written in English that is targeted at people with dementia, but this information is not readable by a general audience. This is problematic considering that people with <12 years of education are at a higher risk of developing dementia. Further, our findings demonstrate that digital dementia information written in English has a negative tone, which may be a contributing factor to the mental health crisis many people with dementia face after receiving a diagnosis. Therefore, we call for content creators to lower readability scores to make the information more accessible to a general audience and to focus their efforts on providing information in a way that does not perpetuate overly negative narratives of dementia.

Assessment of sentence sophistication in L2 spoken production: Expansion of verbs and argument structure constructions

Despite the vast body of literature concerning automatic analyses of L2 production, there is limited research on L2 spoken production. Furthermore, investigations into L2 sentence production abilities have predominantly been centered around sentence length. To provide more sophisticated descriptions of L2 syntactic sophistication, this study examines the validity of using the expansion of verbs and constructions as an index of L2 oral production abilities, drawing upon the principles of usage-based approaches to language development. Employing natural language processing tools, we analyzed 790 speech samples produced by L2 English learners using frequency-based and construction-focused indices to assess their predictive validity in relation to the learners’ speaking proficiency. Our findings revealed that advanced L2 learners tend to exhibit an increased use of diverse construction types and a wider range of verb types within each construction. Notably, among the individual construction types observed, the transitive and there-expletive constructions exerted the most substantial influence on the variance in speaking proficiency. These findings suggest a close relationship between the development of verbs and constructions in the spoken domain and factors related to language usage. The results provide an opportunity to evaluate L2 sentence production skills in speaking within the theoretical framework of usage-based approaches.

Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review

The advent of many popular commercial forms of natural language processing tools has changed the way we can utilise digital technologies to tackle problems with big data. The objective of this review is to evaluate the current research and landscape of natural language processing tools and explore their potential use and impact in the field of orthopaedic surgery. In doing so, this review aims to answer the research question of how NLP tools can be utilised to streamline processes within orthopedic surgery. To do this, a scoping review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Arksey and O’Malley framework for scoping reviews, as well as a computer-assisted literature search on the Medline, Embase and Google Scholar databases. Papers that evaluated the use of natural language processing tools in the field of orthopaedic surgery were included. Our literature search identified 24 studies that were eligible for inclusion. Our scoping review captured articles that highlighted multiple uses of NLP tools in orthopaedics. In particular, one study reported on the use of NLP for intraoperative monitoring, six for detection of adverse events, five for establishing orthopaedic diagnoses, two for assessing the patient experience, two as an informative resource for patients, one for predicting readmission, one for triaging, five for auditing and one for billing and coding. All studies assessed these various uses of NLP through its tremendous computational ability in extracting structured and unstructured text from the medical record, including operative notes, pathology and imaging reports, and progress notes, for use in orthopaedic surgery. Our review demonstrates that natural language processing tools are becoming increasingly studied for use and integration within various processes of orthopaedic surgery. These AI tools offer tremendous promise in improving efficiency, auditing and streamlining tasks through their immense computational ability and versatility. Despite this, further research to optimise and adapt these tools within the clinical environment, as well as the development of evidence-based policies, guidelines and frameworks are required before their wider integration within orthopaedics can be considered.

OSPAR: A Corpus for Extraction of Organic Synthesis Procedures with Argument Roles.

There is a pressing need for the automated extraction of chemical reaction information because of the rapid growth of scientific documents. The previously reported works in the literature for the procedure extraction either (a) did not consider the semantic relations between the action and argument or (b) defined a detailed schema for the extraction. The former method was insufficient for reproducing the reaction, while the latter methods were too specific to their own schema and did not consider the general semantic relation between the verb and argument. In addition, they did not provide an annotated text that aligned with the structured procedure. Along these lines, in this work, we propose a corpus named organic synthesis procedures with argument roles (OSPAR) that is annotated with rolesets to consider the semantic relation between the verb and argument. We also provide rolesets for chemical reactions, especially for organic synthesis, which represent the argument roles of actions in the corpus. More specifically, we annotated 112 organic synthesis procedures in journal articles from Organic Syntheses and defined 19 new rolesets in addition to 29 rolesets from an existing language resource (Proposition Bank). After that, we constructed a simple deep learning system trained on OSPAR and discussed the usefulness of the corpus by comparing it with chemical description language (XDL) generated by a natural language processing tool, namely, SynthReader. While our system's output required more detailed parsing, it covered comparable information against XDL. Moreover, we confirmed that the validation of the output action sequence was easy as it was aligned with the original text.

Identification of social scientifically relevant topics in an interview repository: a natural language processing experiment

PurposeThe present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.Design/methodology/approachThe authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.FindingsThe study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.Originality/valueInterview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.

Summary of the 2nd Natural Language-based Software Engineering Workshop (NLBSE 2023)

Natural language processing (NLP) involves the automated anal- ysis and manipulation of human language. This includes algo- rithms that can analyze text created by humans and algorithms that can generate text that appears natural. Nowadays, NLP methods are becoming increasingly prevalent to enhance various aspects of software development. Indeed, throughout the software development lifecycle, numerous natural language artifacts are produced. Therefore, the existence of NLP-based approaches and tools has opened up possibilities for improving the e ectiveness and productivity of software engineers, processes, and products. The second edition of the Natural Language-Based Software Engi- neering Workshop (NLBSE) took place in 2023 alongside the 45th International Conference on Software Engineering (ICSE 2023), where the research community engaged in discussions about these approaches. This event brought together researchers and practi- tioners from the elds of NLP and software engineering to ex- change experiences, establish future research directions, and pro- mote the adoption of NLP techniques and tools in tackling chal- lenges speci c to software engineering. In this paper, we present a summary of the 2nd edition of the workshop, which comprised three full papers, four short/position papers, ve tool competi- tion/demonstration papers, two keynote talks (\Automated Bug Management: Re ections &amp; the Road Ahead" by David Lo and \Trends and Opportunities in the Application of Large Language Models: the Quest for Maximum E ect" by Albert Ziegler), fol- lowed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2023.github.io/index. html

Comparison of category and letter fluency tasks through automated analysis.

Category and letter fluency tasks are commonly used neuropsychological tasks to evaluate lexical retrieval. This study used validated automated methods, which allow for more expansive investigation, to analyze speech production of both category ("Animal") and letter ("F") fluency tasks produced by healthy participants (n = 36) on an online platform. Recordings were transcribed and analyzed through automated pipelines, which utilized natural language processing and automatic acoustic processing tools. Automated pipelines calculated overall performance scores, mean inter-word response time, and word start time; errors were excluded from analysis. Each word was rated for age of acquisition (AoA), ambiguity, concreteness, frequency, familiarity, word length, word duration, and phonetic and semantic distance from its previous word. Participants produced significantly more words on the category fluency task relative to the letter fluency task (p < 0.001), which is in line with previous studies. Wilcoxon tests also showed tasks differed on several mean speech measures of words, and category fluency was associated with lower mean AoA (p<0.001), lower frequency (p < 0.001), lower semantic ambiguity (p < 0.001), lower semantic distance (p < 0.001), lower mean inter-word RT (p = 0.03), higher concreteness (p < 0.001), and higher familiarity (p = 0.02), compared to letter fluency. ANOVAs significant interactions for fluency task on total score and lexical measures showed that lower category fluency scores were significantly related to lower AoA and higher prevalence, and this was not observed for letter fluency scores. Finally, word-characteristics changed over time and significant interactions were noted between the tasks, including word familiarity (p = 0.019), semantic ambiguity (p = 0.002), semantic distance (p=0.001), and word duration (p<0.001). These findings showed that certain lexical measures such as AoA, word familiarity, and semantic ambiguity were important for understanding how these tasks differ. Additionally, it found that acoustic measures such as inter-word RT and word duration are also imperative to analyze when comparing the two tasks. By implementing these automated techniques, which are reproducible and scalable, to analyze fluency tasks we were able to quickly detect these differences. In future clinical settings, we expect these methods to expand our knowledge on speech feature differences that impact not only total scores, but many other speech measures among clinical populations.

Improving Neurology Clinical Care With Natural Language Processing Tools.

The integration of natural language processing (NLP) tools into neurology workflows has the potential to significantly enhance clinical care. However, it is important to address the limitations and risks associated with integrating this new technology. Recent advances in transformer-based NLP algorithms (e.g., GPT, BERT) could augment neurology clinical care by summarizing patient health information, suggesting care options, and assisting research involving large datasets. However, these NLP platforms have potential risks including fabricated facts and data security and substantial barriers for implementation. Although these risks and barriers need to be considered, the benefits for providers, patients, and communities are substantial. With these systems achieving greater functionality and the pace of medical need increasing, integrating these tools into clinical care may prove not only beneficial but necessary. Further investigation is needed to design implementation strategies, mitigate risks, and overcome barriers.

Analyzing the Symptom Clusters Among Children With Acute Lymphoblastic Leukemia During Chemotherapy Based on Electronic Nursing Records.

Children with acute lymphoblastic leukemia often experience various physical and psychological symptoms during chemotherapy. Previous studies have used predetermined symptom inventories to analyze the complex relationships between these symptoms, which has certain limitations. The aim of this study was to explore the dynamic changes in symptom clusters among children with acute lymphoblastic leukemia during chemotherapy using electronic nursing records. Electronic nursing records for 2021 (N = 14 490) were obtained from a pediatric hematology department. A natural language processing tool was used to identify the presence of 46 symptoms in 3 chemotherapy phases of induction, consolidation, and maintenance. Incidence of symptom documentation was calculated by notes and patients, and principal component analysis and cluster analysis were performed for symptoms with an incidence above 15%. The most common symptoms included bleeding, cough, and vomiting. The number of symptom clusters in each chemotherapy period was 7, 6, and 4, respectively. There were upper gastrointestinal, respiratory system, lower gastrointestinal, skin-related, psychological, self-image disorder, and other discomfort symptom clusters in the induction period. Notably, the upper gastrointestinal, respiratory system, lower gastrointestinal, and skin-related symptom clusters persisted through the consolidation and maintenance periods. Neurological and other discomfort symptom clusters were observed during the consolidation period. Symptom clusters of children with acute lymphoblastic leukemia at various stages of chemotherapy showed a certain level of stability and dynamics, informing healthcare providers in developing continuous symptom management. Medical staff should explore targeted intervention programs based on characteristics of symptom clusters at different chemotherapy stages, to improve the efficiency of symptom management and alleviate the symptom burden of children.