WIREs Data Mining and Knowledge Discovery最新文献

{"title":"Explainable Artificial Intelligence for Clinical Trust in Parkinson's Disease: A Scoping Review on Non‐Invasive Digital Biomarkers","authors":"Lorenzo Rettori, Erika Rovini, Hamido Fujita, Filippo Cavallo","doi":"10.1002/widm.70131","DOIUrl":"https://doi.org/10.1002/widm.70131","url":null,"abstract":"Parkinson's disease (PD) increasingly relies on non‐invasive digital biomarkers and artificial intelligence (AI) methods for early diagnosis, symptom monitoring, and disease management. However, the growing use of complex machine learning and deep learning models introduces challenges related to model opacity, limiting clinical interpretability and potentially hindering adoption in healthcare settings. Explainable artificial intelligence (XAI) has emerged as a potential approach for improving transparency and supporting understanding of model behavior. This scoping review examined the application of XAI techniques in AI systems based on low‐cost, non‐invasive digital biomarkers for PD. Following PRISMA‐ScR guidelines, literature searches were performed in Scopus, PubMed, and Web of Science, identifying 36 eligible studies published between 2021 and 2025. Extracted data included clinical application, data modality, AI models, XAI approaches, explanation characteristics, explainability application, and explanation evaluation strategies. The reviewed studies showed widespread adoption of post hoc attribution‐based methods, particularly SHAP and LIME, across applications including diagnosis, symptom assessment, disease progression monitoring, and treatment‐state identification. XAI was commonly employed for feature ranking, biomarker exploration, model optimization, and explanatory analysis. However, explainability evaluation was highly heterogeneous, with most studies relying primarily on descriptive interpretation or plausibility assessments. Formal evaluation of explanation fidelity, clinician‐centered usability studies, and prospective clinical validation were largely absent. Current evidence suggests that XAI in PD research provides methodological tools that may support transparency and model understanding, but evidence for clinical trust, usability, and real‐world deployment remains limited. The field appears methodologically active but still immature for routine clinical implementation.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review on Vertical and Horizontal Association Rule Mining Algorithms","authors":"Vinod Chandra S S, Anand H S","doi":"10.1002/widm.70129","DOIUrl":"https://doi.org/10.1002/widm.70129","url":null,"abstract":"Association rule mining (ARM) is a fundamental technique in data mining, widely employed to uncover significant relationships and latent patterns within large‐scale transactional datasets. Since the introduction of the Apriori algorithm, ARM methodologies have evolved substantially to address the challenges presented by increasingly voluminous and complicated data, as well as emerging application requirements. Numerous algorithms, including ECLAT, FP‐Growth, tertius, utility‐based mining, sequential pattern mining, and graph mining approaches, have been designed to improve computing performance, scalability, and the quality of extracted patterns. In recent years, the advent of Big Data, distributed computing, explainable artificial intelligence (XAI), knowledge graphs, federated learning, and large language models (LLMs) has significantly expanded the scope of ARM beyond classic market basket analysis. This review thoroughly examines the progression of ARM, providing a comparative study of both classical and contemporary algorithms in terms of their search strategies, data representations, candidate generation approaches, computational characteristics, advantages, limitations, and application domains. The performance of ARM algorithms is assessed through a case study. Recent advancements in distributed and streaming ARM, graph pattern mining, high‐utility and sequential pattern mining, knowledge graph rule learning, and explainable rule discovery are also discussed. Emerging trends such as privacy‐preserving mining, AI‐assisted rule generation, and neuro‐symbolic knowledge discovery are also discussed to identify future research directions. Through integrating fundamental techniques with recent developments in artificial intelligence and large‐scale data analytics, this survey aims to guide researchers and practitioners in choosing suitable ARM algorithms and identifying viable avenues for future work within scalable, interpretable, and intelligent knowledge discovery.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Steganography and Steganalysis: A Comprehensive Review on the Future of Hidden Communication","authors":"Mounika Tummala,Jatindra Kumar Dash,Aditya Kumar Sahu","doi":"10.1002/widm.70117","DOIUrl":"https://doi.org/10.1002/widm.70117","url":null,"abstract":"ABSTRACT As the digital landscape continues to expand, the secure concealment and retrieval of information have become equally critical for ensuring confidentiality and integrity. Steganography and steganalysis serve as a cornerstone for securing and retrieving sensitive information and facilitating covert communication, while effectively mitigating potential threats. Despite significant advancements in digital communication technologies, ensuring information security remains an ongoing challenge, particularly in the context of neural network (NN)‐based steganography and steganalysis architectures. This study presents a comprehensive analysis of steganography and steganalysis methods, evolving from classical to advanced intelligence frameworks by emphasizing image quality, security of hidden information, payload capacity, and robust detection mechanisms against adversarial perturbations. By leveraging state‐of‐the‐art methods such as convolutional neural network (CNN), generative adversarial network (GAN), autoencoders, and fuzzy logic (FL) for security, imperceptibility, robustness, and the ability to address ongoing challenges related to payload capacity, adaptive information embedding and retrieval, and computational efficiency. This study explores the fields of steganography and steganalysis, focusing on embedding and extraction methods from conventional methods (non‐neural network [N‐NN]) to NN architectures. The insights presented serve as a foundational reference for transitioning from conventional embedding and decoding approaches to advanced NN‐driven techniques, thereby enhancing security, efficiency, and resilience in covert communication systems.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic Analysis of Machine Learning, Deep Learning, and Hybrid Models for Android and\u0000 IoT\u0000 Ecosystems: A Review","authors":"Ahsan Wajahat,Kailong Zhang,Jahanzaib Latif,Abdul Ahad","doi":"10.1002/widm.70119","DOIUrl":"https://doi.org/10.1002/widm.70119","url":null,"abstract":"ABSTRACT The widespread adoption of Android and the Internet of Things (IoT) devices has led to a substantial increase in malware threats, challenging the effectiveness of traditional signature‐based security mechanisms. The dynamic and evolving nature of modern malware requires intelligent and adaptive detection techniques, making machine learning (ML) and deep learning (DL) approaches increasingly important. This paper presents a systematic survey of ML and DL‐based malware detection techniques, analyzing existing studies based on learning models, feature selection, datasets, and evaluation metrics. Conventional ML algorithms and advanced DL architectures including CNNs, RNNs, autoencoders, and hybrid models are critically compared. Our analysis confirms that DL and hybrid approaches outperform traditional ML methods for complex threats, though their accuracy and F1‐score depend heavily on dataset quality, feature engineering, and tuning. We synthesize widely used datasets and evolving malware families. However, despite strong experimental results, significant challenges remain, including dataset imbalance, IoT resource constraints, limited interpretability, and generalization gaps that hinder real‐world deployment. This study concludes that future work should prioritize developing lightweight, adaptive, and explainable detection frameworks that are specifically designed to operate under the operational constraints of Android and IoT platforms.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148894838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Text Anonymization: A Systematic Review","authors":"Marina Litvak, Alípio Jorge","doi":"10.1002/widm.70127","DOIUrl":"https://doi.org/10.1002/widm.70127","url":null,"abstract":"Text anonymization has become a critical requirement across healthcare, legal, financial, and online communication domains, where large volumes of sensitive textual data are increasingly used for analytics, information retrieval, and model training. Despite decades of research, anonymization remains challenging due to the diversity of sensitive information types, domain‐specific annotation schemes, and the emergence of complex inference risks that extend beyond explicit identifiers. On the other hand, we have seen a large number of approaches using transformers in recent years. This survey provides the first comprehensive and systematic review of text anonymization methods published between 2020 and 2025, covering 48 primary studies identified through a structured search and rigorous screening procedure. We analyze anonymization approaches across rule‐based, statistical, neural, transformer‐based, and large language model (LLM) paradigms, and compare them across multiple domains, languages, and sensitive‐information taxonomies. Our review also synthesizes available datasets, software resources, and evaluation practices used to assess both privacy protection and text utility. The findings highlight significant fragmentation across domains, persistent limitations in handling quasi‐identifiers and semantic leakage, and substantial inconsistencies in evaluation protocols. We identify key methodological trends, gaps, and emerging challenges, including the integration of LLMs, multilingual settings, and adversarial evaluation. Our analysis reveals a growing shift from identifier‐centric de‐identification toward context‐aware anonymization, while evaluation methodologies have not yet evolved at the same pace. We outline open research directions and propose a roadmap toward more robust, context‐aware, and empirically grounded anonymization systems. This survey aims to establish a unified reference point for researchers and practitioners working on privacy‐preserving NLP and sensitive text processing.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Interactive New‐Media Arts Applications in Managing Knowledge in the Public Domain","authors":"Diaa Ahmed Mohamed Ahmedien","doi":"10.1002/widm.70125","DOIUrl":"https://doi.org/10.1002/widm.70125","url":null,"abstract":"Interactive new media arts (INMA) are technologically oriented artistic practices that share with knowledge management (KM) a concern for managing data, information, and knowledge (DIK). Despite this overlap, KM has largely overlooked INMA's practices—a gap compounded by the absence of a KM framework addressing INMA's contributions to KM in the public domain. Drawing on three interconnected theories, this review proposes a KM framework that examines how INMA applications' organizational interactions manage DIK across generative, participatory, and integrative management contexts. The framework is structural rather than normative: it identifies the operational conditions under which INMA manages DIK, not whether those outcomes are epistemically superior. A PRISMA‐guided analysis of 239 INMA studies (2000–2024) examined these contexts to characterize how INMA broadens KM in the public domain. The analysis shows that INMA's organizational interactions create dialogues between explicit and tacit DIK in the generative context and amplify them through expanding interactions in the participatory and integrative contexts. These operations consolidate into three aligned triads: tiers of interaction (passive, retroactive, and active), knowledge outcomes (receptive, dialogic, and co‐constitutive), and DIK relationship models (separate, interconnected, and unified). Synthesizing across the models yields a paradigm in which the active tier corresponds to the unified model to produce co‐constitutive knowledge—the configuration most capable of extending KM into open, public settings, yet the least frequently realized in the corpus. Compared with traditional KM, the paradigm characterizes INMA's distinctive approach to open DIK acquisition, flow, and codification and outlines the associated benefits and challenges.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review of Data Science in Early Voice‐Based Detection of Parkinson's and Amyotrophic Lateral Sclerosis Neurodegenerative Diseases","authors":"Abdulazeez Mousa, Fatih Özyurt, Ridwan Boya Marqas Shamoun, Laszlo Barna Iantovics","doi":"10.1002/widm.70123","DOIUrl":"https://doi.org/10.1002/widm.70123","url":null,"abstract":"Recent studies have shown that voice analysis provides a feasible, non‐invasive diagnostic alternative for the early detection of neurodegenerative diseases (NDs), such as Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS), using Artificial Intelligence (AI), primarily machine learning models (MLs) that can be integrated into complex clinical decision support systems (CDSSs). Although significant progress has been made, several computational challenges remain, including dataset variability, noisy and incomplete input data, and the need for robust generalization across linguistic and demographic differences. To overcome these issues, system and model reliability must be improved through extensive testing under realistic conditions and integration with clinical workflows. This comprehensive study reviews the potential of AI to analyze speech biomarkers for accurate early detection and diagnosis. Improvements over traditional methods have been achieved through the use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Methods (EMs), and diverse hybrid models. However, adoption of these models in real clinical practice is limited by challenges in standardization and effective real‐world applicability. Effective adoption of highly efficient AI‐based voice analysis in clinical practice requires standardized methods, collaboration among specialists from different disciplines, and evidence from real clinical trials. Physicians require comprehensive explanations of AI models to develop confidence in AI‐powered diagnostic tools. Explainable Artificial Intelligence (XAI) applications for neurodegenerative disease diagnosis remain underexplored in research, despite the need for healthcare practitioners to understand diagnostic predictions. Our study provides insights for researchers, software developers, and clinical practitioners, highlighting the need for future research on the development of robust, adaptive models that can accommodate data errors and remain fair and private across AI‐based diagnostics. Future research must build transparent AI models able to achieve high accuracy alongside interpretability standards for standard medical practice deployment. The review also systematically evaluates the quality of the data underlying the models that are reported, including the adequacy of the sample size, class balance, linguistic diversity, and recording conditions. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Algorithmic Development &gt; Biological Data Mining </jats:list-item> <jats:list-item> Application Areas &gt; Health Care </jats:list-item> <jats:list-item> Technologies &gt; Computational Intelligence </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Patient‐Centric Healthcare Systems: A Systematic Review of Blockchain‐Based Solutions","authors":"Shuvadip Roy, J. Sathish Kumar","doi":"10.1002/widm.70124","DOIUrl":"https://doi.org/10.1002/widm.70124","url":null,"abstract":"The increasing digitalization of healthcare systems demands secure, transparent, and interoperable data management solutions. Conventional centralized healthcare infrastructures have several significant drawbacks, such as the risk of breaches, poor interoperability between different applications, little or no control for patients over access to their own data, and reliance upon third‐party providers who can be untrustworthy. Due to the decentralized nature of its architecture and its resistance to tampering, blockchain technology offers the potential to create healthcare solutions that address each of these issues by enhancing trust and accountability within the healthcare community. This paper is a systematic review of recent research into blockchain‐based healthcare solutions which includes an examination of recent research development, technological frameworks, and current trends. In addition to classifying existing research according to application domains (such as Electronic Medical Record Management, IoT based Health Monitoring, Pharmaceutical Supply Chain Traceability, Insurance Fraud Detection, Disease Prediction) this paper also examines various aspects of blockchain technologies including platforms, consensus mechanisms, cryptographic techniques, and architectural models, while highlighting their strengths and limitations. Finally, this paper highlights several open challenges including scalability, interoperability, regulatory compliance, and limited real‐world deployments, and presents several possible research directions toward creating patient‐centric healthcare systems.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148688077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Exploring Large Language Model‐Based Intelligent Agents: Definitions, Methods, and Prospects”","authors":"","doi":"10.1002/widm.70126","DOIUrl":"https://doi.org/10.1002/widm.70126","url":null,"abstract":"","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148675247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Medical Information Fusion: A Review of Deep Learning Models and Their Applications","authors":"Haohan Wu, Yaping Xu, Zhiduo Zhang, Daguang Jiang, Hui Li","doi":"10.1002/widm.70120","DOIUrl":"https://doi.org/10.1002/widm.70120","url":null,"abstract":"Recent advancements in deep learning have transformed medical image processing, particularly in integrating multimodal data and optimizing complex tasks, positioning fusion models as the leading paradigm in contemporary research. Historically viewed as mere pixel‐level blending, this field has fundamentally evolved into a broader paradigm of Medical Information Fusion. This review systematically explores deep learning‐driven advancements, focusing on their theoretical foundations, key technological breakthroughs and typical application scenarios. First, we systematically categorize fusion architectures into data‐level, feature‐level, and decision‐level hierarchies across diverse modalities. Second, we address key technical challenges and methodological breakthroughs, focusing on advanced solutions for domain shift and semantic alignment, handling data scarcity via missing modality completion, and task‐specific optimization through multi‐task learning. Furthermore, we critically examine the clinical transitional potential of these models in key clinical scenarios, including advanced tumor diagnosis, smart surgery, personalized therapy, and brain function analysis. By synthesizing these insights, this work aims to provide a comprehensive understanding of the current landscape and future directions of medical information fusion, paving the way for advancements in precision medicine and improved healthcare outcomes.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148675164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}