Frontiers in Big Data最新文献

{"title":"Basrah Score: a novel machine learning-based score for differentiating iron deficiency anemia and beta thalassemia trait using RBC indices.","authors":"Salma A Mahmood, Asaad A Khalaf, Saad S Hamadi","doi":"10.3389/fdata.2025.1634133","DOIUrl":"10.3389/fdata.2025.1634133","url":null,"abstract":"<p><p>Iron deficiency anemia (IDA) and beta-thalassemia trait (BTT) are prevalent causes of microcytic anemia, often presenting overlapping hematological features that pose diagnostic challenges and necessitate prompt and precise management. Traditional discrimination indices-such as the Mentzer Index, Ihsan's formula, and the England and Fraser criteria-have been extensively applied in both research and clinical settings; however, their diagnostic performance varies considerably across different populations and datasets. This study proposes a novel and interpretable diagnostic model, the Basrah Score, developed using Elastic Net Logistic Regression (ENLR). This machine learning-based approach yields a flexible discrimination function that adapts to variations in clinical and environmental factors. The model was trained and validated on a local dataset of 2,120 individuals (1,080 with IDA and 1,040 with BTT), and was benchmarked against eight conventional indices. The Basrah Score demonstrated superior diagnostic performance, with an accuracy of 96.7%, a sensitivity of 95.0%, and a specificity of 98.6%. These results underscore the importance of incorporating advanced pre-processing techniques, class balancing, hyperparameter optimization, and rigorous cross-validation to ensure the robustness of diagnostic models. Overall, this research highlights the potential of integrating interpretable machine learning models with established clinical parameters to improve diagnostic accuracy in hematological disorders, particularly in resource-constrained settings.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1634133"},"PeriodicalIF":2.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12358405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884292","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":"The global burden of adverse effects of medical treatment: a 30-year socio-demographic and geographic analysis using GBD 2021 data.","authors":"Hanxin Lu, Xinyan Cheng, Jun Xiong","doi":"10.3389/fdata.2025.1590551","DOIUrl":"10.3389/fdata.2025.1590551","url":null,"abstract":"<p><strong>Background: </strong>Adverse effects of medical treatment (AEMT) pose critical global health challenges, yet comprehensive analyses of their long-term burden across socio-demographic contexts remain limited. This study evaluates 30-year trends (1990-2021) in AEMT-related mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) across 204 countries using Global Burden of Disease (GBD) 2021 data.</p><p><strong>Methods: </strong>Age-standardized rates (ASRs) were stratified by sociodemographic index (SDI) quintiles. Frontier efficiency analysis quantified health loss boundaries relative to SDI, while concentration (C) and slope indices of inequality (SII) assessed health inequities. Predictive models projected trends to 2035.</p><p><strong>Results: </strong>Global age-standardized mortality rates (ASDR) declined by 36.3%, with low-SDI countries achieving the steepest reductions (5.31 to 3.71/100,000) but remaining 3.9-fold higher than high-SDI nations. DALYs decreased by 39.7% (106.49 to 64.19/100,000), driven by infectious disease control in low-SDI regions. High-SDI countries experienced post-2010 mortality rebounds (0.86 to 0.95/100,000), linked to aging and complex interventions. YLLs declined by 40.3% (104.87 to 62.66/100,000), while YLDs peaked transiently (2010: 1.95/100,000). Frontier analysis revealed low-SDI countries lagged furthest from optimal health outcomes, and inequality indices highlighted entrenched disparities (C: -0.34 for premature mortality). Projections suggest continued declines in ASDR, DALYs, and YLLs by 2035, contingent on addressing antimicrobial resistance and surgical overuse.</p><p><strong>Conclusions: </strong>SDI-driven inequities necessitate tailored interventions: low-SDI regions require strengthened infection control and primary care, while high-SDI systems must mitigate overmedicalization risks. Hybrid strategies integrating digital health and cross-sector collaboration are critical for equitable burden reduction.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1590551"},"PeriodicalIF":2.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12354518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144876764","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":"OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.","authors":"Fatema-E Jannat, Sina Gholami, Minhaj Nur Alam, Hamed Tabkhi","doi":"10.3389/fdata.2025.1609124","DOIUrl":"10.3389/fdata.2025.1609124","url":null,"abstract":"<p><strong>Introduction: </strong>In the medical AI field, there is a significant gap between advances in AI technology and the challenge of applying locally trained models to diverse patient populations. This is mainly due to the limited availability of labeled medical image data, driven by privacy concerns. To address this, we have developed a self-supervised machine learning framework for detecting eye diseases from optical coherence tomography (OCT) images, aiming to achieve generalized learning while minimizing the need for large labeled datasets.</p><p><strong>Methods: </strong>Our framework, OCT-SelfNet, effectively addresses the challenge of data scarcity by integrating diverse datasets from multiple sources, ensuring a comprehensive representation of eye diseases. By employing a robust two-phase training strategy self-supervised pre-training with unlabeled data followed by a supervised training stage, we utilized the power of a masked autoencoder built on the SwinV2 backbone.</p><p><strong>Results: </strong>Extensive experiments were conducted across three datasets with varying encoder backbones, assessing scenarios including the absence of self-supervised pre-training, the absence of data fusion, low data availability, and unseen data to evaluate the efficacy of our methodology. OCT-SelfNet outperformed the baseline model (ResNet-50, ViT) in most cases. Additionally, when tested for cross-dataset generalization, OCT-SelfNet surpassed the performance of the baseline model, further demonstrating its strong generalization ability. An ablation study revealed significant improvements attributable to self-supervised pre-training and data fusion methodologies.</p><p><strong>Discussion: </strong>Our findings suggest that the OCT-SelfNet framework is highly promising for real-world clinical deployment in detecting eye diseases from OCT images. This demonstrates the effectiveness of our two-phase training approach and the use of a masked autoencoder based on the SwinV2 backbone. Our work bridges the gap between basic research and clinical application, which significantly enhances the framework's domain adaptation and generalization capabilities in detecting eye diseases.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1609124"},"PeriodicalIF":2.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838516","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":"Collaborative filtering based on nonnegative/binary matrix factorization.","authors":"Yukino Terui, Yuka Inoue, Yohei Hamakawa, Kosuke Tatsumura, Kazue Kudo","doi":"10.3389/fdata.2025.1599704","DOIUrl":"10.3389/fdata.2025.1599704","url":null,"abstract":"<p><p>Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix factorization techniques such as nonnegative matrix factorization (NMF) are often employed. Nonnegative/binary matrix factorization (NBMF), which is an extension of NMF, approximates a nonnegative matrix as the product of nonnegative and binary matrices. While previous studies have applied NBMF primarily to dense data such as images, this paper proposes a modified NBMF algorithm tailored for collaborative filtering with sparse data. In the modified method, unrated entries in the rating matrix are masked, enhancing prediction accuracy. Furthermore, utilizing a low-latency Ising machine in NBMF is advantageous in terms of the computation time, making the proposed method beneficial.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1599704"},"PeriodicalIF":2.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838515","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":"LLM-as-a-Judge: automated evaluation of search query parsing using large language models.","authors":"Mehmet Selman Baysan, Serkan Uysal, İrem İşlek, Çağla Çığ Karaman, Tunga Güngör","doi":"10.3389/fdata.2025.1611389","DOIUrl":"https://doi.org/10.3389/fdata.2025.1611389","url":null,"abstract":"<p><strong>Introduction: </strong>The adoption of Large Language Models (LLMs) in search systems necessitates new evaluation methodologies beyond traditional rule-based or manual approaches.</p><p><strong>Methods: </strong>We propose a general framework for evaluating structured outputs using LLMs, focusing on search query parsing within an online classified platform. Our approach leverages LLMs' contextual reasoning capabilities through three evaluation methodologies: Pointwise, Pairwise, and Pass/Fail assessments. Additionally, we introduce a Contextual Evaluation Prompt Routing strategy to improve reliability and reduce hallucinations.</p><p><strong>Results: </strong>Experiments conducted on both small- and large-scale datasets demonstrate that LLM-based evaluation achieves approximately 90% agreement with human judgments.</p><p><strong>Discussion: </strong>These results validate LLM-driven evaluation as a scalable, interpretable, and effective alternative to traditional evaluation methods, providing robust query parsing for real-world search systems.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1611389"},"PeriodicalIF":2.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12319771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144785911","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":"Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.","authors":"Fangling Wang, Azlan Mohd Zain, Yanjie Ren, Mahadi Bahari, Azurah A Samah, Zuraini Binti Ali Shah, Norfadzlan Bin Yusup, Rozita Abdul Jalil, Azizah Mohamad, Nurulhuda Firdaus Mohd Azmi","doi":"10.3389/fdata.2025.1624507","DOIUrl":"10.3389/fdata.2025.1624507","url":null,"abstract":"<p><p>This review systematically summarizes recent advances in microarray feature selection techniques and their applications in biomedical research. It addresses the challenges posed by the high dimensionality and noise of microarray data, aiming to integrate the strengths and limitations of various methods while exploring their applicability across different scenarios. By identifying gaps in current research, highlighting underexplored areas, and proposing clear directions for future studies, this review seeks to inspire academics to develop novel techniques and applications. Furthermore, it provides a comprehensive evaluation of feature selection methods, offering both a theoretical foundation and practical guidance to help researchers select the most suitable approaches for their specific research questions. Emphasizing the importance of interdisciplinary collaboration, the study underscores the potential of feature selection in transformative applications such as personalized medicine, cancer diagnosis, and drug discovery. Through this review, not only does it provide in-depth theoretical support for the academic community, but also practical guidance for the practical field, which significantly contributes to the overall improvement of microarray data analysis technology.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1624507"},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709875","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":"Optimizing public health management with predictive analytics: leveraging the power of random forest.","authors":"Hongman Wang, Yifan Song, Hua Bi","doi":"10.3389/fdata.2025.1574683","DOIUrl":"10.3389/fdata.2025.1574683","url":null,"abstract":"<p><p>Community health outcomes significantly impact older populations' wellbeing and quality of life. Traditional analytical methods often struggle to accurately predict health risks at the community level due to their inability to capture complex, non-linear relationships among various health determinants. This study employs a Random Forest Algorithm (RFA) to address this limitation and enhance the predictive modeling of community health outcomes. By leveraging ensemble learning techniques and multi-factor analysis, this study aims to identify and quantify the relative contributions of key health indicators to risk assessment. The study begins with comprehensive data collection from diverse health sources, followed by a systematic preprocessing stage, which includes resolving missing values, normalizing variables, and encoding categorical features. Using bootstrap sampling, multiple decision trees were trained on random subsets of health data, ensuring variability in the model learning. The trees grow to full depth and aggregate their predictions to enhance the accuracy. An out-of-bag (OOB) error estimation was applied to refine the model and provide unbiased performance assessments, ensuring robust generalization to unseen data. The proposed model effectively analyzes key health indicators, ranking the feature importance to determine the most influential predictors of health risks. Results indicate that RFA achieves an accuracy rate of 92%, outperforming conventional prediction methods in terms of precision and recall. These findings underscore the efficacy of Random Forest in identifying critical health risk factors, paving the way for targeted and data-driven public health management strategies and interventions tailored to older adults.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1574683"},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709876","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":"Design and development of an efficient RLNet prediction model for deepfake video detection.","authors":"Varad Bhandarkawthekar, T M Navamani, Rishabh Sharma, K Shyamala","doi":"10.3389/fdata.2025.1569147","DOIUrl":"10.3389/fdata.2025.1569147","url":null,"abstract":"<p><strong>Introduction: </strong>The widespread emergence of deepfake videos presents substantial challenges to the security and authenticity of digital content, necessitating robust detection methods. Deepfake detection remains challenging due to the increasing sophistication of forgery techniques. While existing methods often focus on spatial features, they may overlook crucial temporal information distinguishing real from fake content and need to investigate several other Convolutional Neural Network architectures on video-based deep fake datasets.</p><p><strong>Methods: </strong>This study introduces an RLNet deep learning framework that utilizes ResNet and Long Short Term Memory (LSTM) networks for high-precision deepfake video detection. The key objective is exploiting spatial and temporal features to discern manipulated content accurately. The proposed approach starts with preprocessing a diverse dataset with authentic and deepfake videos. The ResNet component captures intricate spatial anomalies at the frame level, identifying subtle manipulations. Concurrently, the LSTM network analyzes temporal inconsistencies across video sequences, detecting dynamic irregularities that signify deepfake content.</p><p><strong>Results and discussion: </strong>Experimental results demonstrate the effectiveness of the combined ResNet and LSTM approach, showing an accuracy of 95.2% and superior detection capabilities compared to existing methods like EfficientNet and Recurrent Neural Networks (RNN). The framework's ability to handle various deepfake techniques and compression levels highlights its versatility and robustness. This research significantly contributes to digital media forensics by providing an advanced tool for detecting deepfake videos, enhancing digital content's security and integrity. The efficacy and resilience of the proposed system are evidenced by deepfake detection, while our visualization-based interpretability provides insights into our model.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1569147"},"PeriodicalIF":2.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700334","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":"AI-powered smart emergency services support for 9-1-1 call handlers using textual features and SVM model for digital health optimization.","authors":"Afraa Attiah, Manal Kalkatawi","doi":"10.3389/fdata.2025.1594062","DOIUrl":"10.3389/fdata.2025.1594062","url":null,"abstract":"<p><p>In emergency situations, 9-1-1 is considered the first point of contact, and their call handlers play a crucial role in managing the emergency response. Due to the large number of daily calls and the hectic routine, there are severe chances that the call handlers can make any mistake or human error during data taking in a high-pressure environment. These mistakes or errors impact 9-1-1 performance in emergencies. To address this problem, this research introduces an AI-powered digital health framework called Emergency Calls Assistant (ECA) that leverages artificial intelligence (AI) and natural language processing (NLP) techniques to assist call handlers during data collection. ECA is designed to predict the type of emergency, suggest relevant questions to collect deeper information, suggest pre-arrival instructions to emergency personnel, and generate incident reports that helps in data-driven decision making. The ECA framework works in two phases; the first phase is to convert the audio call into digital textual form, and the second phase is to analyze the textual information using NLP tools and mining techniques to retrieve contextual information. The second phase also deals with emergency categorization using a support vector machine (SVM) learning model to prioritize the emergency dealing with an accuracy of 92.7%. The key factors involved in categorization by ML models are the severity of injury and weapons involvement. The objective of ECA's development is to provide digital health-saving technology to 9-1-1 call handlers and save lives by making accurate decisions by providing real-time assistance. This research aligns with the advancement of digital health technologies by exhibiting how NLP-driven decision support systems can revolutionize emergency healthcare, improve patient outcomes through real-time AI integration, and reduce errors.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1594062"},"PeriodicalIF":2.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683593","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":"Fine-tuning or prompting on LLMs: evaluating knowledge graph construction task.","authors":"Hussam Ghanem, Christophe Cruz","doi":"10.3389/fdata.2025.1505877","DOIUrl":"10.3389/fdata.2025.1505877","url":null,"abstract":"<p><p>This paper explores Text-to-Knowledge Graph (T2KG) construction, assessing Zero-Shot Prompting, Few-Shot Prompting, and Fine-Tuning methods with Large Language Models. Through comprehensive experimentation with Llama2, Mistral, and Starling, we highlight the strengths of FT, emphasize dataset size's role, and introduce nuanced evaluation metrics. Promising perspectives include synonym-aware metric refinement, and data augmentation with Large Language Models. The study contributes valuable insights to KG construction methodologies, setting the stage for further advancements.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1505877"},"PeriodicalIF":2.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602189","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}