Natural Language Research Articles

Performance of ChatGPT-3.5 and ChatGPT-4o in the Japanese National Dental Examination.

In this study, we compared the performance of ChatGPT-3.5 to that of ChatGPT-4o in the context of the Japanese National Dental Examination, which assesses clinical reasoning skills and dental knowledge, to determine their potential usefulness in dental education. ChatGPT's performance was assessed using 1399 (55% of the exam) of 2520 questions from the Japanese National Dental Examinations (111-117). The 1121 excluded questions (45% of the exam) contained figures or tables that ChatGPT could not recognize. The questions were categorized into 18 different subjects based on dental specialty. Statistical analysis was performed using SPSS software, with McNemar's test applied to assess differences in performance. A significant improvement was noted in the percentage of correct answers from ChatGPT-4o (84.63%) compared with those from ChatGPT-3.5 (45.46%), demonstrating enhanced reliability and subject knowledge. ChatGPT-4o consistently outperformed ChatGPT-3.5 across all dental subjects, with significant improvements in subjects such as oral surgery, pathology, pharmacology, and microbiology. Heatmap analysis revealed that ChatGPT-4o provided more stable and higher correct answer rates, especially for complex subjects. This study found that advanced natural language processing models, such as ChatGPT-4o, potentially have sufficiently advanced clinical reasoning skills and dental knowledge to function as a supplementary tool in dental education and exam preparation.

A Web Crawling and NLP-Powered Model for Filtering Inappropriate Content for Primary School Learners' Online Research

Most of learning today has gone digital as online methods are being utilized. A lot of activities happen on the internet, people post various material which are both good and bad. However, students whilst studying end up accessing those bad websites like porn sites and other inappropriate content. Ensuring a safe online environment for students is very vital in order for them not to be disturbed or to end up being victims on the internet. Parents and teachers have tried to monitor activities of their children but end up being tricked. Some researchers suggest blocking the unsafe sites which students end up by-passing. This research proposed use of a web crawling and Natural Language Processing powered model for filtering inappropriate content for primary school online learners. The results obtained indicated that inappropriate content was blacklisted, filtered successfully and could not be accessed by students. Therefore, the model was developed correctly and met the intended research goal.

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

DSL‐Driven Approaches and Metamodels for Chatbot Development: A Systematic Literature Review

ABSTRACTChatbots have emerged as ubiquitous tools for enhancing user interaction across various platforms, from customer service to personal assistance. They are computer programs that simulate and process human conversation, either written, spoken or both. However, developing efficient chatbots remains a challenge, primarily due to the intricate nature of critical components of chatbots like natural language understanding (NLU) requiring a subscription from intent recognition providers like Dialogflow and Amazon Lex. This makes chatbots closely linked to NLP services and can be locked in. Recently, various research studies have provided solutions to reduce the workload of developers and designers. These approaches have proposed model‐driven development via domain‐specific languages (DSLs), which make the chatbot development process more accessible and more automated. This advancement aims to enhance effectiveness in chatbot development by leveraging DSLs. This study aims to provide a comprehensive overview of DSLs for developing chatbots, with the first contribution comprising various research topics, tools, approaches, and technologies employed to implement DSLs. Second, this work aims to assess and contrast the primary DSLs currently available for chatbot development, focusing on presenting the key elements used in constructing these DSLs. Third, this study identifies the challenges and limitations of using DSLs in chatbot development.

Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods.

Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people worldwide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary. In natural language processing applications, the word embedding association test (WEAT) is a popular method of measuring bias in input embeddings, a common area of measure bias in AI. However, certain limitations of the WEAT have been identified (ie, their nonrobust measure of bias and their reliance on predefined and limited groups of words or sentences), which may lead to inadequate measurements and evaluations of bias. Thus, this study takes a new approach at modifying this popular measure of bias, with a focus on making it more robust and applicable in other domains. In this study, we introduce the SD-WEAT, which is a modified version of the WEAT that uses the SD of multiple permutations of the WEATs to calculate bias in input embeddings. With the SD-WEAT, we evaluated the biases and stability of several language embedding models, including Global Vectors for Word Representation (GloVe), Word2Vec, and bidirectional encoder representations from transformers (BERT). This method produces results comparable to those of the WEAT, with strong correlations between the methods' bias scores or effect sizes (r=0.786) and P values (r=0.776), while addressing some of its largest limitations. More specifically, the SD-WEAT is more accessible, as it removes the need to predefine attribute groups, and because the SD-WEAT measures bias over multiple runs rather than one, it reduces the impact of outliers and sample size. Furthermore, the SD-WEAT was found to be more consistent and reliable than its predecessor. Thus, the SD-WEAT shows promise for robustly measuring bias in the input embeddings fed to AI language models.

The Role Of Machine Learning In Transforming Healthcare: A Systematic Review

This systematic review explores the transformative role of machine learning (ML) in healthcare, focusing on its applications in disease diagnosis, personalized medicine, healthcare operations, and patient monitoring. By analyzing 167 peer-reviewed studies published between 2015 and 2024, the review highlights how ML algorithms have significantly improved early disease detection, treatment personalization, and operational efficiencies in healthcare settings. Findings reveal that ML-powered diagnostic tools, especially deep learning models, demonstrate accuracy levels comparable to human experts in areas like cancer detection. Additionally, ML models tailored for personalized medicine have shown promise in optimizing treatment protocols based on genetic profiles, reducing adverse reactions and improving patient outcomes. In healthcare operations, ML applications have streamlined hospital resource management, enhanced workflow efficiency, and reduced administrative burdens through predictive analytics and natural language processing (NLP). Moreover, ML-driven remote patient monitoring systems have enabled proactive interventions for chronic disease management. Despite these advancements, challenges related to data privacy, algorithmic transparency, and regulatory compliance persist. Addressing these barriers is critical for realizing the full potential of ML in healthcare, ensuring ethical deployment and equitable access. The review concludes by emphasizing the need for robust regulatory frameworks and further research to enhance the integration of ML technologies into clinical practice.

Exploring knowledge management technologies to enhance sustainability and mitigate technostress from a collaborative perspective

Purpose The relevance of digital technology and knowledge management (KM) has been increasing continuously owing to their importance in sustainability. However, research on which technology leads to collaboration in KM and how it affects sustainability is lacking. Therefore, this study aims to analyze knowledge collaboration management technologies for sustainable growth based on academic literature and technology patents. Design/methodology/approach This paper analyzed current technology and academic trends in KM using BERTopic, an artificial intelligence natural language processing model, and proposed future promising candidate technologies and potential application areas in terms of collaboration and sustainability. Findings From the textual data of research papers and patents, this paper extracted 10 and 47 major topics, respectively. Moreover, five potential and feasible candidate patents along with their applicable fields were recommended. Originality/value The strength of this study is its analysis of the technologies affecting collaboration and sustainability in KM. This paper found that new digital technologies (i.e. artificial intelligence, cloud computing and big data) impact organizations’ KM and have the potential for collaboration. Digital technologies have a positive impact on corporate smartness directly and sustainability and inclusion indirectly. This paper argued that organizations should understand and identify various promising technological factors and apply them to their KM from a collaboration perspective.

Abstract 4140118: Identification of Obstructive and Non-Obstructive Hypertrophic Cardiomyopathy Patients Using Natural Language Processing in a Large Integrated Healthcare System

Introduction: Accurately identifying and characterizing patients with hypertrophic cardiomyopathy (HCM) is critical for population management and care optimization. Research Question: To develop natural language processing (NLP) algorithms to identify and characterize obstructive (oHCM) and non-obstructive (nHCM) HCM patients directly from echocardiograms, and to compare with the presence or absence of HCM-related diagnosis codes. Methods: We developed and validated NLP algorithms to identify HCM from all adult (age≥18yrs) echocardiograms performed from 2010-2019 in Kaiser Permanente Northern CA (KPNC), capturing measures of any HCM, HCM subtype, hypertrophy subtype, septal and posterior LV wall thickness, resting and stress/Valsalva LVOT gradients, and systolic anterior motion. We developed a rules-based algorithm (following AHA/ACC criteria) to classify patients as having HCM, including oHCM or nHCM subtypes, and possible HCM (defined as wall thickness ≥2cm without other criteria meeting an HCM definition). We evaluated the presence of HCM-related ICD-9/10 diagnosis codes among patients classified as HCM/non-HCM from echocardiograms using NLP, and linked baseline demographics and clinical parameters from our integrated electronic medical record. Results: Among 472,405 adults with echocardiograms, we identified 2,892 patients with HCM based upon NLP-derived measures (all NLP measures achieved >95% positive predictive value and >95% negative predictive value), including 1,585 (55%) with oHCM, 1,145 (40%) with nHCM, and 162 (6%) which could not be classified (Figure). Among those 2,892 patients, 1,283 did not have any associated HCM ICD-9/10 diagnosis codes (Table). Among 469,513 patients with no identified HCM from NLP-based algorithms, HCM ICD-9/10 diagnosis codes existed in 1,567 patients (Table). We also identified 4,593 patients with possible HCM by NLP, only 4.5% of whom had an associated HCM code. Among confirmed HCM patients by NLP, oHCM patients were slightly older (66 vs 61 yrs), more likely female (53% vs 43%), had similar mean septal wall thickness (1.7cm vs 1.7cm), but were more likely to have a septal hypertrophy subtype (46% vs 28%) compared to nHCM patients. Conclusions: Echocardiogram-based NLP methods can improve the identification of and care for HCM patients. Many patients with possible HCM may be underdiagnosed, representing an opportunity for quality improvement.

Analyzing the Correlation Between Toxic Comments and Code Quality

ABSTRACTSoftware development has a relevant human side, and this could, for example, imply that developers' feelings have an impact on certain aspects of software development such as quality, productivity, or performance. This paper explores the effects of toxic emotions on code quality and presents the SentiQ tool, which gathers and analyzes sentiments from commit messages (obtained from GitHub) and code quality measures (obtained from SonarQube). The SentiQ tool we proposed performs a sentiment analysis (based on natural language processing techniques) and relates the results to the code quality measures. The datasets extracted are then used as the basis on which to conduct a preliminary case study, which demonstrates that there is a relationship between toxic comments and code quality that may affect the quality of the whole software project. This has resulted in the drafting of a predictive model to validate the correlation of the impact of toxic comments on code quality. The main implication of this work is that these results could, in the future, make it possible to estimate code quality as a function of developers' toxic comments.

How Czech students perceive action and time in their mother tongue: A case study

Based on 207 questionnaires that investigated how undergraduate students of Czech Philology perceive action and time, it was found that their understanding of these terms varies. Students’ interpretation was influenced not only by the knowledge they had and the language rules they applied, but also by their intuition and natural language perception, which inherently complement the process. We concluded that more attention should be paid to deverbal adjectives because their recognition proved to be particularly challenging.

Big Data Analytics And Predictive Analysis In Enhancing Customer Relationship Management (CRM): A Systematic Review Of Techniques And Tools

This systematic review examines the transformative role of big data analytics and predictive techniques in Customer Relationship Management (CRM), focusing on how these advancements enhance customer engagement, satisfaction, and retention strategies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzed a final selection of 100 articles, exploring CRM applications such as predictive modeling, real-time data processing, data integration, and Natural Language Processing (NLP). Findings indicate that predictive analytics enables CRM systems to anticipate customer behaviors and personalize interactions, while real-time data processing supports immediate responses to customer needs, essential in high-demand sectors. Data integration helps overcome silos, creating a cohesive customer view that enhances personalized engagement across all touchpoints. NLP applications, including sentiment analysis and chatbots, further improve CRM by fostering empathy and responsiveness in customer interactions. Despite these advancements, challenges related to data privacy and regulatory compliance remain central, necessitating strict data protection and ethical handling practices. This review underscores that while CRM technology is evolving to meet modern demands, effective implementation depends on a balance between technical innovation and adherence to ethical standards in data management, ultimately fostering meaningful and sustainable customer relationships.

Abstract 4144529: Cardiac Allograft Hypertrophy (CAH) at 6 Months Post Transplant Is a Prognostic Marker of Long-Term Outcomes: Are We Missing a Simple Biomarker?

Introduction: Cardiac hypertrophy is a known adverse prognostic marker in various non-transplant pathologies. In heart transplant patients, due to many confounders, it has been controversial on the relevance and timing of cardiac hypertrophy as an adverse remodeling vs acute injury pattern in an immunologically hostile environment. Previous studies have shown prognostication of hypertrophy on echocardiogram at 1-year post-heart transplant. Research Questions: Does cardiac hypertrophy within a year after transplant have long-term prognostic implications? Methods: We collected relevant clinical variables for all heart transplants using EPIC EHR’s Clarity database. Hypertrophy was defined based on LV Mass Indexed to body surface area where LV Mass = 0.8 x (1.04 x (((LVIDD + IVSd + PWd) 3 - LVIDD 3 ))) + 0.6. Relative Wall Thickness was defined as RWT = 2 x PWd / LVIDD. We used a rule-based natural language processing program validated by correlation with manual readings by trained cardiologists (r=0.96, p=0.007) to abstract echo variables. Results: Inclusion criteria were heart transplants performed from 2015 to 2023 at our center, with an echocardiogram closest to 6 months (+/- 1 month). Ten percent (n=33) showed hypertrophy on echocardiograms at 6 months (Table 1). Of these, 20 (61%) had mild, 3 (9%) severe, and 10 (30%) moderate hypertrophy. Of 33 patients, 28 (85%) had concentric, and 5 (15%) had eccentric hypertrophy. Patients with hypertrophy at 6 months had significantly worse survival at 5 years (p=0.01) and 10 years (p=0.05) compared to patients without hypertrophy (Fig 1). Survival at 5 and 10 years was not statistically different for patients with hypertrophy at 3 months (5 yrs p=0.17, 10 yrs p=0.06), 12 months (5 yrs p=0.38, 10 yrs p=0.30), and 18 months (5 yrs p=0.15, 10 yrs p=0.08) compared to those without hypertrophy. Conclusion: Cardiac hypertrophy on echocardiogram at 6 months predicts adverse long-term survival, while other time points did not.

Abstract 4137559: Assessing Veterans’ Risk of Cardiovascular Disease by Gender Identity

Background: Extant literature suggests that transgender and gender diverse (TGD) individuals experience a higher burden of CVD risk factors and psychosocial stressors than their cisgender (cis) peers. However, prior studies have been limited by self-reported outcomes or small samples. Aim: To determine whether TGD veterans have greater CVD (MI, ischemic stroke, cardiac revascularization) incidence than cis veterans using large, nationwide data. Methods: We used EHR data from the Veterans Healthcare Administration to identify veterans with >2 outpatient encounters in fiscal years 2010-2019. Veterans’ gender identity was classified using natural language processing in combination with gender-affirming hormone therapy (GAHT) and a previously validated algorithm for identifying TGD individuals using ICD codes and VHA data. Of 1,105,082 veterans without prior CVD, 42,149 were classified as TGD. We examined sample characteristics by gender identity and used Cox regression to assess the association of gender identity with incident CVD. Results: TGD veterans’ mean age (years) was 46 (cis females=40; cis males=53). Cis females had the fewest CVD risk factors, followed by TGD veterans and cis males. Adjusting for age, race, and Hispanic ethnicity, TGD veterans had 1.49 [1.42-1.56] and 0.93 [0.90-0.97] times the risk of CVD compared to cis females and cis males, respectively. Upon additional adjustment for other CVD risk factors, renal function, alcohol use disorder, depression, anxiety, and sexual minority status, results remained statistically significant. Significance also held in sensitivity analyses which matched each TGD veteran to cis female and male veterans on age within 2 years. In analyses stratified by gender identity, trans feminine (HR [95% CI]: 1.26 [1.14-1.40]) and uncategorized TGD veterans (HR [95% CI]: 1.29 [1.22-1.37]) were at higher CVD risk than cis female veterans; trans masculine (HR [95% CI]: 0.85 [0.77-0.93]) and trans feminine veterans (HR [95% CI]: 0.90 [0.82-0.99]) were at lower CVD risk than cis male veterans. Among TGD veterans, GAHT receipt was associated with an 11% decrease in CVD risk. Conclusion: TGD veterans’, particularly trans feminine and uncategorized TGD veterans’, CVD risk was greater than that of cis females. Contrary to prior findings, GAHT was associated with decreased CVD risk. These results invite further study into the mechanisms by which GAHT and other factors impact CV health across and within gender identities.

Modernizando a Redação Policial: Protótipo de Aplicativo Android para a Elaboração de Relatos de Ocorrências

This article presents the development of an Android application prototype designed to assist Military Police officers with writing incident reports. The application leverages Google's Gemini AI API to provide real-time grammar correction and intelligent suggestions for writing improvement, acting as an experienced officer reviewing and enhancing the text. The app's architecture, based on the MVVM (Model-View-ViewModel) pattern, and the user interface design, focused on simplicity and usability, are detailed. The implementation of the Gemini AI API, including the system prompt that defines the AI's role and behavior, is explored, along with cost analysis and comparison with other popular natural language processing APIs like OpenAI and Anthropic. Test results using real incident reports demonstrate the app's ability to improve text clarity, conciseness, and professionalism, as well as provide relevant suggestions for police practice, such as missing information, legal procedures, and evidence collection. The article discusses the impacts and benefits of the application for the PMPR, the limitations of the technology, and suggestions for future work, including integration with other platforms and expansion of AI functionalities.

Image–Text Matching Model Based on CLIP Bimodal Encoding

Image–text matching is a fundamental task in the multimodal research field, connecting computer vision and natural language processing by aligning visual content with corresponding textual descriptions. Accurate matching is critical for applications such as image captioning and text-based image retrieval yet remains challenging due to the differences in data modalities. This paper addresses these challenges by proposing a robust image–text matching model inspired by Contrastive Language–Image Pre-training (CLIP). Our approach employs the Vision Transformer (ViT) model as the image encoder and Bidirectional Encoder Representations from Transformers (Bert) as the text encoder, integrating these into a shared vector space to measure semantic similarity. We enhance the model’s training efficiency using the LiT-tuning paradigm to optimize learning through a cosine decay strategy for dynamic adjustment of the learning rate. We validate our method on two benchmark datasets, WuKong and Flickr30k, demonstrating that our model achieves superior performance and significantly improves key evaluation metrics. The results underscore the model’s effectiveness in achieving accurate and robust image–text alignment.

Effective Communication of System-Level Events for Hospital System Health and Nurse Well-Being: A Qualitative Study

Many injuries and deaths occur yearly in the United States due to preventable errors; however, documented harm is lower in hospitals with Magnet® designation from the American Nurses Credentialing Center (Silver Spring, MD, USA). This manuscript illustrates insights from Magnet® and non-Magnet® nurses and nursing leaders regarding what impacts hospital system health, how those factors are communicated, and how hospital system health impacts patient safety. A qualitative descriptive approach was used with semi-structured interview questions. Within-methods data triangulation was applied to transcribed interviews using thematic analysis, application of the Goodwin statistic, and natural language processing (NLP). Thematic analysis revealed the four main themes of Healthy Systems, Unhealthy Systems, Patient Safety, and Nurse Well-Being. NLP revealed non-Magnet® leaders and nurses scored highest for anxiety, anger, and sadness, in contrast to Magnet® participants who had the highest levels of positive emotion. Key findings from the Magnet® participants revealed that poaching employees and placing people in the wrong positions harm hospital system health and patient safety. The use of within-methods data triangulation illuminated the contextual depth of the participants’ speech, revealing the alarming emotional state that non-Magnet® nurses and leaders are facing. Healthcare staff’s wellness must be prioritized to cultivate environments that provide safe care.

A INTELIGÊNCIA ARTIFICAL COMO FERRAMENTA DE EFICIÊNCIA NA ADMINISTRAÇÃO PÚBLICA

The article “Artificial Intelligence as a Tool for Efficiency in Public Administration” explores the growing role of AI in optimizing government operations and improving the delivery of public services. Focusing on AI’s capabilities to process and analyze large volumes of data, the article discusses how this technology can be used to enhance efficiency, transparency, and responsiveness in government services. The text highlights several key applications of AI in public administration, including large-scale data analysis and processing, automation of bureaucratic processes, and customization of services to citizens. These technologies, equipped with machine learning and natural language processing, enable better resource management, faster citizen service, and more efficient operations less susceptible to human errors. Beyond the benefits, the article also addresses the challenges associated with implementing AI in the public sector, such as technical, ethical, and privacy issues. Concerns about algorithmic bias, data quality, and integration with legacy systems are discussed in detail. The article emphasizes the need for robust policies and adequate training for public employees as essential for successful AI integration. Finally, the discussion concludes with the view that while AI has the potential to significantly transform public administration for the better, a careful and regulated approach is vital. The success of this integration depends not only on technological advancements but also on a commitment to ethical practices and the development of public policies that guide its responsible use.

Detecting Fake News Using Data Analytics: A Systematic Literature Review And Machine Learning Approach

This study systematically reviews advancements in fake news detection techniques, examining methodologies and frameworks across machine learning, natural language processing, social network analysis, and multimodal approaches. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous and transparent review process was conducted, resulting in the selection and evaluation of 254 research articles. The findings reveal that supervised learning models, such as Support Vector Machines, Decision Trees, and Naïve Bayes, have shown strong performance in text-based fake news classification, particularly when feature selection is optimized. Deep learning models, including CNNs, RNNs, and transformers, have further advanced detection accuracy by capturing complex linguistic patterns, though challenges with computational demands and model interpretability remain. In scenarios with limited labeled data, unsupervised learning models and semi-supervised approaches offer adaptability, with clustering, anomaly detection, and iterative self-labeling proving effective for evolving misinformation. Additionally, cross-disciplinary approaches, integrating insights from psychology, sociology, and network science, enhance detection models by accounting for user behavior, emotional appeal, and social conformity in the spread of fake news. Case studies from collaborative projects underscore the potential of interdisciplinary efforts to develop robust, adaptable detection frameworks. This review concludes that effective fake news detection requires a multifaceted approach, combining technical advancements with social science insights to address the complexity and adaptive nature of misinformation. The study emphasizes the need for continued research in hybrid models and adaptive, real-time detection solutions to strengthen defenses against fake news in diverse digital environments.

Abstract 4136509: Children in Foster Care with Heart Disease Have Greater Resource Utilization and Mortality

Background: While children and youth in foster care (CYFC) have greater healthcare needs generally, data are lacking in heart disease. We characterize CYFC with heart disease, assess the impacts of being in foster care on outcomes, and examine validity of ICD-10 code Z62.21 for identification of CYFC in administrative data. Methods: In this single-center, retrospective cohort study of all cardiac surgical patients (<18yo) admitted 2017-2022, CYFC were identified using institutional Electronic Medical Records via natural language processing or ICD codes, with confirmation by chart review. Demographics, medical characteristics and outcomes of CYFC were compared to children not in foster care. Generalized linear models assessed associations with primary outcomes of mortality at current follow-up (logit link) and total hospital days (log link), controlling for clinical factors and length of follow-up. We separately added Child Opportunity Index (COI) and payer to models. This cohort was then studied in the administrative pediatric health information system (PHIS) dataset to calculate ICD code sensitivity and specificity. Results: Of 4,431 cardiac surgical patients, 67 (1.5%) were in foster care. CYFC did not have significantly higher mortality (9% vs 5.8%), 30-day readmission or cardiac reoperation, number of complex chronic conditions (CCC), RACHS-2, or distance from center compared to non-CYFC. CYFC had lower COI (median 61 vs 72, p=0.016) and more Medicaid coverage (87% vs 40%, p<0.001). Length of stay after index surgery was similar; however, cumulative hospital days during the study were significantly higher for CYFC (median 22 [IQR 10,45] vs 12 [6,27], p<0.001). Adjusting for RACHS-2, CCC, age, readmission, reoperation, and follow-up duration, CYFC had higher odds of mortality (2.7 [95%CI 1.0,6.9] p=0.046) and more total hospital days (p=0.008). With COI or payer in the model, CYFC was not associated with mortality but remained an independent risk factor for more total hospital days. Only 16/67 (23.9%) of CYFC were coded with the Z62.21 in PHIS, yielding sensitivity of 21.9% (95%CI 13.1,33.1), and specificity of 100% (99.9,100). Conclusion: CYFC with heart disease have a relative social disadvantage, greater hospital resource utilization, and long-term mortality despite similar illness severity. Improved capture of foster care status in administrative data is needed to guide future studies and reduce health disparities for the vulnerable population of CYFC.

Unravelling the knowledge matrix: exploring knowledge-sharing behaviours on market-based platforms using regression tree analysis

PurposeThis study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.Design/methodology/approachThe research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.FindingsCognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.Originality/valueThis research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.