Frontiers in Big Data最新文献

{"title":"Generation of Kazakhstan's unified national testing variants using AI: a platform for automatic task creation with expert control.","authors":"Bolatbek Abdrasilov, Talgat Niyazov, Lyazzat Shinetova, Shugyla Altybayeva, Kenzhekul Turalbayeva, David Orlov","doi":"10.3389/fdata.2026.1772101","DOIUrl":"10.3389/fdata.2026.1772101","url":null,"abstract":"<p><p>This study examines the use of artificial intelligence for Automatic Item Generation (AIG) in the context of Kazakhstan's Unified National Testing (UNT) and presents a human-in-the-loop platform for scalable, expert-controlled test development. The objective is to evaluate whether large language models (LLMs) can reliably generate high-quality, isomorphic mathematics test items in the Kazakh language while preserving psychometric and pedagogical requirements. A hybrid AI system combining a local and a cloud-based LLM was implemented to perform semantic deconstruction of prototype items and constrained isomorphic generation of new variants. The pipeline included structured prompt engineering, parallel generation, and automated symbolic validation using Python and SymPy, followed by double-blind expert review. A stratified sample of 120 UNT mathematics items served as prototypes, from which 200 AI-generated clones were produced and validated. Six qualified subject-matter experts conducted independent evaluations using standardized criteria. Inter-rater reliability reached a substantial level (Cohen's κ = 0.78). Results show that 97.5% of generated items were recommended for use after review, with 50.5% accepted without revision and 47.0% accepted after corrections. The most frequent revision needs involved difficulty calibration, wording clarity, and factual or curricular alignment. Expert interviews confirmed that AI generation significantly reduces development time but remains limited in higher-order cognitive item design and pedagogically grounded distractor construction, especially in a low-resource, morphologically complex language environment. The findings support a hybrid augmentation model in which AI accelerates large-scale item production while experts ensure linguistic, cultural, and psychometric validity. The proposed framework demonstrates practical potential for multilingual, high-stakes assessment systems and provides implementation guidelines for responsible AI integration in test development.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1772101"},"PeriodicalIF":3.8,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the impact of OMOP-CDM on data quality insight generation in respiratory disease management.","authors":"Brenda Mbouamba Yankam, Fankoua Tchaptchet Luc Baudoin, Pauline Andeso, François Anicet Onana Akoa, Jean Blaise Ebimbe, Miranda Barasa, Mbele Onana, Samuel Iddi, Agnes Kiragga, Bertrand Hugo Mbatchou Ngahane","doi":"10.3389/fdata.2026.1744885","DOIUrl":"10.3389/fdata.2026.1744885","url":null,"abstract":"<p><p>The increasing volume and heterogeneity of patient care data present significant challenges for comprehensive analysis and the generation of insights, particularly in specific areas such as respiratory diseases. Standardizing diverse health data is crucial for enabling large-scale observational research and ensuring data readiness. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a widely adopted standard for harmonizing such data. However, evaluating the quality of data transformed into the OMOP CDM format is a critical step before its use in research or clinical decision support. This study evaluates the impact of the OMOP CDM standardization process on generating data quality insights for a respiratory disease dataset. The source dataset was initially paper-based, converted to an electronic format, and translated from French into English. This historical dataset covers the years 2009-2023 and contains 108 variables and 2,154 records. The data underwent the standard Extract, Transform, and Load (ETL) process to convert into the OMOP CDM format. Following this transformation, the quality of the resulting OMOP CDM instance was assessed. We utilized the Data Quality Dashboard (DQD) to evaluate the quality of the OMOP CDM database before and after ETL verification. DQD performs validation checks on the data based on key data quality dimensions, including completeness, plausibility, and conformance. Overall, the assessment conducted 2,344 checks, of which 2,269 passed, and 75 failed, resulting in a corrected pass rate of 96% for the Respiratory Diseases Inpatients data before ETL verification. After ETL verification, the assessment conducted 2,374 checks, of which 2,356 passed, and 40 failed, resulting in a 100% corrected pass rate. Standardizing respiratory disease data using the OMOP CDM enabled a structured and transparent evaluation of data quality. Through the application of the DQD, this study demonstrated the utility of OMOP CDM in generating meaningful data quality insights. These findings highlight the model's potential to enhance data readiness and support evidence-based decision-making in respiratory disease management.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1744885"},"PeriodicalIF":3.8,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13106076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.","authors":"Kunhua Zhong, Han Chen, Qilong Sun, Peng Wang, Zhenbei Liu, Yuwen Chen","doi":"10.3389/fdata.2026.1811110","DOIUrl":"10.3389/fdata.2026.1811110","url":null,"abstract":"<p><strong>Background: </strong>Sepsis is a major cause of postoperative morbidity and mortality, and early risk stratification from perioperative electronic health records (EHR) is a representative large-scale, high-dimensional data processing problem that requires models to be accurate, efficient, and clinically interpretable. However, many existing sepsis prediction methods operate as black boxes and rely on extensive temporal monitoring streams, which increases feature dimensionality and computation while limiting transparency.</p><p><strong>Methods: </strong>We propose a reinforcement learning-guided, interpretable feature engineering framework for postoperative sepsis prediction that targets scalable learning on heterogeneous perioperative data. Within an Actor-Critic formulation, feature selection is treated as an action: an Actor network produces a stochastic feature mask over preoperative static variables and intraoperative statistical summaries, while a Critic network performs downstream prediction using a self-attention-based classifier. To benchmark and stabilize learning, we introduce an auxiliary baseline model that incorporates intraoperative temporal signals extracted by a temporal convolutional network (TCN) and regularized using the Hilbert-Schmidt Independence Criterion (HSIC) to encourage non-redundant representations between statistical and temporal feature views. The Actor is optimized to achieve comparable predictive performance to the baseline while using a reduced feature set, improving computational efficiency and supporting instance-level interpretability.</p><p><strong>Results: </strong>Experiments on a real-world surgical cohort from Southwest Hospital (2014-2018) demonstrate that the proposed framework attains performance comparable to or better than competitive machine learning baselines while selecting fewer input features. On this dataset, our method achieved perfect scores of 1.00 for F1-score, Sensitivity, and Specificity.</p><p><strong>Conclusion: </strong>The proposed method accurately predicts the occurrence of postoperative sepsis and provides effective instance-level post hoc explanations. These findings offer a novel perspective for postoperative sepsis prediction.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1811110"},"PeriodicalIF":3.8,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A disease potential-driven graph attention model for comorbidity risk prediction of hypertension.","authors":"Leming Zhou, Hanshu Qin, Yanmei Yang, Gang Huang, Zhigang Liu","doi":"10.3389/fdata.2026.1814157","DOIUrl":"10.3389/fdata.2026.1814157","url":null,"abstract":"<p><p>Hypertension is associated with an increased risk of serious complications, and the hazards are very serious. However, current methods for predicting comorbidity risks face the challenge that comorbidity prediction relying solely on data driven may lead to clinically implausible associations and reduce model interpretability. Also, how to capture the fusion features of patient and identify differences among them to facilitate risk prediction needs to be addressed. To overcome these challenges, we propose a Disease Potential-Driven Graph Attention (DP-GA) model for comorbidity risk prediction of hypertension, which has 3-fold ideas: (a) Constructing a fusion mechanism for the correlation among the patients' disease features and the structural, thus integrating feature attention and structural attention effectively; (b) Introducing a similarity-difference balance mechanism to further identify the relationships among patients; and (c) Designing a disease potential-driven attention mechanism to calculate the disease potential and construct masks, thus preserving the effective associations from high-risk patients to low-risk patients. Experimental results demonstrate that our proposed DP-GA model achieves a significant improvement in comorbidity risk prediction for patients with hypertension across three comorbidity datasets collected by the research group, compared with both the baseline and state-of-the-art peer methods. We also analyze the comorbidity network to predict the risk of hypertension comorbidity, thereby improving interpretability and early prediction of such comorbidities.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1814157"},"PeriodicalIF":3.8,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward robust social media sentiment for SMEs: a comparative study of dictionary-based and machine learning approaches with insights for hybrid methodologies.","authors":"Heru Susanto, Aida Sari Omar, Alifya Kayla Shafa Susanto, Desi Setiana, Leu Fang-Yie, Junaid M Shaikh, Asep Insani, Uus Khusni, Rachmat Hidayat, Indra Akbari, Iwan Basuki","doi":"10.3389/fdata.2025.1594374","DOIUrl":"10.3389/fdata.2025.1594374","url":null,"abstract":"<p><p>Small and Medium-sized Enterprises (SMEs) increasingly rely on social media to engage customers, promote products, and enhance workplace collaboration. Customer opinions expressed through comments and posts on platforms such as Facebook and Instagram represent valuable insights, yet their informal and context-specific nature-often characterized by slang, misspellings, and bilingual usage-poses challenges for automated sentiment analysis. This study addresses this gap by comparatively evaluating dictionary-based and machine learning approaches to sentiment classification for SMEs' social media content. Data were collected from a diverse set of SMEs across multiple industries, with a substantial volume of customer comments extracted and pre-processed through tokenization, normalization, stop-word removal, and stemming. A customized dictionary was developed to account for local language variations, while Naïve Bayes and Support Vector Machine (SVM) models were employed as supervised classifiers. The findings indicate that dictionary-based methods, while simple and interpretable, struggle with accuracy when processing informal and localized language, whereas machine learning approaches deliver higher overall performance but require extensive preprocessing and tuning. Moreover, the study highlights the potential of hybrid frameworks that combine the interpretability of dictionary-based models with the adaptability of machine learning classifiers. This research contributes both practically and theoretically by (i) demonstrating the limitations of applying generic sentiment analysis tools in localized SME contexts, (ii) proposing a hybrid sentiment analysis framework tailored to SMEs, and (iii) offering empirical evidence to support digital transformation strategies for SMEs in resource-constrained environments. Ultimately, accurate sentiment analysis can enable SMEs to refine business strategies, strengthen customer engagement, and achieve sustainable growth in the digital economy.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1594374"},"PeriodicalIF":3.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13079054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tree-based machine learning methods for predicting vehicle insurance claim size.","authors":"Edossa Merga Terefe, Merga Abdissa Aga","doi":"10.3389/fdata.2026.1778363","DOIUrl":"10.3389/fdata.2026.1778363","url":null,"abstract":"<p><p>Vehicle insurance claim severity modeling requires accurate and interpretable methods that can handle skewed and heterogeneous loss data. This study provides a structured empirical comparison between classical parametric regression models and tree-based ensemble learning approaches for predicting claim size conditional on claim occurrence. The analysis is conducted within a cross-sectional conditional severity framework using real-world motor insurance data. We implement and compare ordinary least squares (OLS), a Tweedie generalized linear model (GLM), and three ensemble methods: bagging, random forests (RFs), and gradient boosting. Model performance is evaluated using out-of-sample root mean square error (RMSE), and variable importance measures assess the relative contribution of predictors. The results indicate that tree-based ensemble methods achieve modest improvements in predictive accuracy relative to classical parametric models. The Tweedie GLM remains a competitive, flexible parametric benchmark for skewed positive claim amounts. Variable importance analysis consistently identifies premium and insured value as key determinants of claim severity. Overall, the findings suggest that ensemble learning methods can complement traditional actuarial models, offering additional flexibility in capturing non-linear effects while maintaining comparable predictive performance in moderate-complexity severity data.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1778363"},"PeriodicalIF":3.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13050731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fairer non-negative matrix factorization.","authors":"Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li","doi":"10.3389/fdata.2026.1737043","DOIUrl":"10.3389/fdata.2026.1737043","url":null,"abstract":"<p><p>There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on \"fair\" PCA, here we consider the more challenging method of non-negative matrix factorization (NMF) as both a showcasing example and a method that is important in its own right for both topic modeling tasks and feature extraction for other ML tasks. We demonstrate that a modification of the objective function, by using a min-max formulation, may <i>sometimes</i> be able to offer an improvement in fairness for groups in the population. We derive two methods for the objective minimization, a multiplicative update rule as well as an alternating minimization scheme, and discuss implementation practicalities. We include a suite of synthetic and real experiments that show how the method may improve fairness while also highlighting the important fact that this may sometime increase error for some individuals and fairness is not a rigid definition and method choice should strongly depend on the application at hand.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1737043"},"PeriodicalIF":3.8,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FunduScope: a human-centered, machine learning-based interactive tool for training junior ophthalmologists in diabetic retinopathy detection.","authors":"Sara-Jane Bittner, Michael Barz, Daniel Sonntag","doi":"10.3389/fdata.2026.1676922","DOIUrl":"10.3389/fdata.2026.1676922","url":null,"abstract":"<p><p>Interpreting fundus images is an essential skill for detecting eye diseases, such as diabetic retinopathy (DR), one of the leading causes of visual impairment. However, the training of junior doctors relies on experienced ophthalmologists, who often lack the time for teaching, or on printed training materials that lack variability in examples. In this work, we present FunduScope, an interactive human-centered learning tool for training junior ophthalmologists, which is based on a pre-trained ML model for classifying DR. In a qualitative pre-study, we investigated the needs of junior doctors and identified gaps in recent learning procedures. In the main mixed-methods study, we examined the experience of 10 junior doctors with the tool and its impact on cognitive load, usability, and additional factors relevant to e-learning tools. Despite technical constraints our results confirm the potential of using an ML-based learning tool in medical education, addressing the time constraints of ophthalmologists, and providing learning independence for junior doctors. However, future work could extend the learning tool by using explainable artificial intelligence (XAI) to further support the clinical decision making of learners and exceeding the scope of this proof of concept to other ophthalmic diseases.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1676922"},"PeriodicalIF":3.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13021406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Jingdezhen ceramic culture in the digital era: a qualitative inquiry into digital dissemination and platform innovation.","authors":"Qiuyang Huang, Zhengjun Chen","doi":"10.3389/fdata.2026.1752142","DOIUrl":"10.3389/fdata.2026.1752142","url":null,"abstract":"<p><strong>Introduction: </strong>Digital platforms have increasingly reshaped the ways in which traditional craft cultures are produced, circulated, and interpreted. While prior research has examined digital heritage broadly, limited attention has been paid to how platform-based dissemination transforms ceramic culture in historically significant craft centers such as Jingdezhen.</p><p><strong>Methods: </strong>This study adopts a qualitative research design, combining semi-structured interviews with 32 ceramic practitioners and digital ethnography of 58 ceramic-related livestreaming sessions on Douyin.</p><p><strong>Results: </strong>The findings reveal three key dynamics: (1) the reconfiguration of craft authority through platform visibility; (2) the emergence of hybrid artisan-educator-entrepreneur identities; and (3) persistent tensions between cultural authenticity and commercial logic in platform-mediated environments.</p><p><strong>Discussion: </strong>By integrating cultural ecology and platform ecosystem theory, this study contributes to scholarship on digital heritage and provides practical insights for cultural practitioners and heritage institutions navigating digital platform ecosystems.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1752142"},"PeriodicalIF":2.4,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13006300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GFTrans: an on-the-fly static analysis framework for code performance profiling.","authors":"Jie Li, Yunbao Wen, Jingxin Liu, Biqing Zeng, Seyedali Mirjalili","doi":"10.3389/fdata.2026.1779935","DOIUrl":"10.3389/fdata.2026.1779935","url":null,"abstract":"<p><p>Improving software efficiency is crucial for maintenance, but pinpointing runtime bottlenecks becomes increasingly difficult as systems expand. Traditional dynamic profiling tools require full build-execution cycles, creating significant latency that impedes agile development. To address this, we introduce GFTrans, a static analysis framework that predicts c program performance without execution. GFTrans utilizes a Transformer architecture with a novel \"anchor-based embedding\" technique to integrate control flow and data dependencies into a unified sequence. Additionally, a dynamic gating mechanism fuses these semantic representations with 16 handcrafted statistical features to comprehensively capture code complexity. Evaluated on a dataset of real-world GitHub c functions with high-precision runtime labels, GFTrans outperforms baseline models like Random Forest and Code2Vec, achieving 78.64% accuracy. The system identifies potential bottlenecks in milliseconds, enabling developers to perform optimization effectively during the coding phase.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1779935"},"PeriodicalIF":3.8,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12982020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}