IEEE Journal of Biomedical and Health Informatics最新文献

{"title":"Estimating Short-Term Heart Rate Variability from Ultra-Short PPG: Mitigating Systematic Bias Using Deep Learning.","authors":"Weinan Wang, Kevin L Kilgore, Laleh Najafizadeh","doi":"10.1109/JBHI.2026.3739353","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3739353","url":null,"abstract":"<p><p>Estimating standard short-term (ST, 5 min) heart rate variability (HRV) metrics (derived from electrocardiogram (ECG)) using ultra-short-term (UST, $< 5$ min) segments of photoplethysmogram (PPG) is desirable for real-time, cost-effective HRV monitoring in critical care settings. Traditional PPG-based HRV estimation relies on pulse rate variability (PRV), which is limited in accuracy due to physiological differences between PPG and ECG signals. Here, we propose a data-driven, end-to-end deep learning approach that estimates ECG-based ST HRV metrics directly from UST PPG segments (as short as 10 s). The key idea is to leverage deep learning to characterize full aspects of the PPG waveform, for learning domain-invariant HRV information shared by short PPG segments and longer ECG recordings. It is shown that the consistent and systematic shift in bias observed in PRV-based HRV estimates, can be mitigated through supervised deep learning using ground-truth ST HRV metrics. HRV estimates from deep learning and PRV-based baselines are compared across 7 ST and UST PPG durations using the VitalDB dataset (4,759 subjects). Models trained on VitalDB are further validated on the BIDMC dataset (24 subjects) to evaluate their generalizability. On the VitalDB dataset, deep learning-based HRV estimates consistently outperform PRV-based baselines as surrogates of ECG-derived ST HRV metrics across time, frequency and nonlinear domains, achieving high correlations ($rho >0.7$) and low biases ($|text{Cohen's } mathrm{d}|< 0.2$) using UST PPG segments of $geq 120$ s duration. Meanwhile, models trained on the VitalDB dataset achieve moderate to high correlation ($rho >0.5$) and low to moderate bias ($|mathrm{d}|< 0.8$) for estimating the majority of ST HRV metrics from $geq 30$ s PPG segments in the BIDMC dataset, showcasing promising cross-dataset generalizability. This study demonstrates the feasibility of using deep learning to infer clinically relevant long-term cardiovascular information from short physiological signals for predictive health monitoring.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149136516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cough-E: A Multimodal, Privacy-Preserving Cough Detection Algorithm for the Edge","authors":"Stefano Albini;Lara Orlandic;Jonathan Dan;Jérôme Thevenot;Tomas Teijeiro;Denisa-Andreea Constantinescu;David Atienza","doi":"10.1109/JBHI.2025.3577507","DOIUrl":"10.1109/JBHI.2025.3577507","url":null,"abstract":"Continuous cough monitors can greatly benefit doctors in home monitoring and treatment of respiratory diseases. Although many works propose algorithms to automate this task, they suffer of poor data privacy and short-term monitoring. Edge-AI is a promising paradigm to overcome these limitations by processing privacy-sensitive data close to their source. However, it presents challenges for the deployment of resource-demanding algorithms on constrained devices. In this work, we propose a hardware-aware methodology for developing a cough detection algorithm, analyzing design-time trade-offs for performance and energy. From audio and kinematic signals, our methodology aims at optimal features via Recursive Feature Elimination with Cross-Validation (RFECV), exploiting the explainability of the selected XGB model. Additionally, it analyzes the use of Mel spectrogram features, instead of the common MFCC. Moreover, a set of hyperparameters for a multimodal implementation of the classifier is explored. Finally, it evaluates the performance based on clinically relevant event-based metrics. The methodology proposes a novel structured approach to efficiently deploy AI on the edge, preserving data privacy. We apply our methodology to develop Cough-E, an energy-efficient, multimodal, and edge AI cough detector. It exploits audio and kinematic data in two distinct models, cooperating for a balanced energy and performance trade-off. We demonstrate that our algorithm can be executed in real-time on an ARM Cortex M33 microcontroller. Cough-E achieves a 70.56% energy saving compared to the audio-only approach, for a 1.26% relative performance drop, resulting in a 0.78 F1-score. Both Cough-E and the edge-aware model optimization methodology are available as open-source code. This approach demonstrates the benefits of the proposed hardware-aware methodology to enable privacy-preserving cough monitors on the edge, paving the way to efficient cough monitoring.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8445-8458"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HGKAN: Hypergraph Kolmogorov-Arnold Networks for Interpretable Prediction of Herb–Symptom Associations","authors":"Xudong Liang;Yamei Huang;Tao Lin","doi":"10.1109/JBHI.2026.3714720","DOIUrl":"10.1109/JBHI.2026.3714720","url":null,"abstract":"With the increasing availability of large-scale traditional Chinese medicine (TCM) data, accurate prediction of herb–symptom associations (HSAs) has become a crucial task in natural drug discovery. Existing computational approaches mainly employ graph neural networks (GNNs) to model herb–symptom relationships in biological information networks. However, these methods are limited to low-order pairwise associations, failing to capture herbs’ high-order effects and providing limited interpretability. To address these challenges, we model HSAs as a hierarchical hypergraph, where ingredients and targets form two distinct node layers, herbs and symptoms are represented as hyperedges connecting multiple nodes, and inter-layer edges capture ingredient–target interactions. Based on this structure, we propose hypergraph Kolmogorov-Arnold networks (HGKAN) comprising two modules: a binary interaction KAN (BiKAN) with bidirectional cross-attention for encoding pairwise ingredient–target interactions, and two high-order interaction KAN (HiKAN) branches for node-hyperedge message passing and selective high-order feature aggregation. Extensive experiments on two public TCM datasets show that HGKAN significantly outperforms 6 state-of-the-art baselines. Case studies further highlight the model’s interpretability and provide mechanistic insights into herbal treatment.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8467-8478"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148706369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Sleep Staging With Bayesian Uncertainty-Guided Active Learning","authors":"Tianyou Yu;Rui Huang;Fei Wang;Jun Zhang;Wei Wu;Zhuliang Yu;Yuanqing Li;Jun Xiao;Zhenghui Gu","doi":"10.1109/JBHI.2026.3678608","DOIUrl":"10.1109/JBHI.2026.3678608","url":null,"abstract":"Automated sleep staging is essential for large-scale and home-based sleep monitoring; however, in routine clinical practice, sleep annotation remains largely dependent on experienced experts performing time-consuming and labor-intensive manual scoring. Existing automatic systems often struggle to adapt reliably to new subjects, limiting their clinical adoption and reinforcing the reliance on expert review. This creates a strong demand for adaptive and efficient sleep staging systems that can substantially reduce annotation workload while preserving expert-level accuracy. We propose BayesSleepNet, a novel framework that integrates Bayesian uncertainty quantification with active learning for adaptive sleep staging. BayesSleepNet employs principled Bayesian modeling by placing distributions over network weights and performing Monte Carlo sampling at inference, enabling explicit quantification of model (epistemic) uncertainty. These uncertainty estimates drive a two-stage sample selection strategy that first fine-tunes the model using representative epochs and subsequently prioritizes persistently uncertain samples for expert review. Across four public sleep datasets, BayesSleepNet consistently improves performance—by 7.60% in accuracy, 8.27% in macro-F1, and 0.104 in Cohen's <inline-formula><tex-math>$kappa$</tex-math></inline-formula>—while requiring manual annotation of only 20% of data from new subjects. Despite its adaptive learning capability, BayesSleepNet remains computationally lightweight, using substantially fewer parameters than representative high-capacity state-of-the-art models. These results demonstrate the clinical promise of uncertainty-aware active learning as a practical and cost-efficient paradigm for semi-automated sleep staging.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8479-8491"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Scale Texture Supplementation for Reference-Based Medical Image Super-Resolution","authors":"Yinghua Li;Weiao Hao;Hao Zeng;Longguang Wang;Jian Xu;Sidheswar Routray;Rutvij H. Jhaveri;Thippa Reddy Gadekallu","doi":"10.1109/JBHI.2025.3572502","DOIUrl":"10.1109/JBHI.2025.3572502","url":null,"abstract":"Magnetic Resonance Imaging (MRI) is a widely used medical imaging technique, but its resolution is often limited by acquisition time constraints, potentially compromising diagnostic accuracy. Reference-based Image Super-Resolution (RefSR) has shown promising performance in addressing such challenges by leveraging external high-resolution (HR) reference images to enhance the quality of low-resolution (LR) images. The core objective of RefSR is to accurately establish correspondences between the reference HR image and the LR images. In pursuit of this objective, this paper develops a Self-rectified Texture Supplementation network for RefSR (STS-SR) to enhance fine details in MRI images and support the expanding role of autonomous AI in healthcare. Our network comprises a texture-specified self-rectified feature transfer module and a cross-scale texture complementary network. The feature transfer module employs high-frequency filtering to facilitate the network concentrating on fine details. To better exploit the information from both the reference and LR images, our cross-scale texture complementary module incorporates the All-ViT and Swin Transformer layers to achieve feature aggregation at multiple scales, which enables high-quality image enhancement that is critical for autonomous AI systems in healthcare to make accurate decisions. Extensive experiments are performed across various benchmark datasets. The results validate the effectiveness of our method and demonstrate that the method produces state-of-the-art performance as compared to existing approaches. This advancement enables autonomous AI systems to utilize high-quality MRI images for more accurate diagnostics and reliable predictions.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8265-8277"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Knowledge-Guided Bi-Modal Network for the Classification of Anterior Chamber Angle Images","authors":"Xiping Jia;Jingqi Huang;Dong Nie;Linan Guan;Jianying Qiu","doi":"10.1109/JBHI.2026.3677046","DOIUrl":"10.1109/JBHI.2026.3677046","url":null,"abstract":"Glaucoma is a leading cause of irreversible blindness globally. When glaucoma is diagnosed, Anterior Chamber Angle (ACA) evaluation is the necessary step for the prognosis and treatment of glaucoma. However, current clinical evaluation methods are labor intensive and rely on expert judgment, which makes them inefficient. Automating ACA classification based on images using machine learning, especially deep neural networks, holds promise. Yet, image samples alone can’t provide sufficient high-level semantic information on ACA, resulting in suboptimal classification performance. This paper proposes a novel end-to-end knowledge-guided bi-modal network (KGNet) for ACA evaluation. Specifically, we consider two modalities of ACA data: textual domain knowledge and images. We first design a new strategy to refine class-based knowledge into textual descriptions, thereby increasing the diversity of features learned by the model. We then extract two types of representations using two distinct components: 1) a supervised loss is applied to learn modality-specific representations by incorporating domain knowledge; 2) a fusion module that uses knowledge-guided learning to highlight key clinical structures in ACA images leveraging bimodal correlations. Experimental results on an ACA dataset and three public datasets show that our method outperforms several state-of-the-art deep learning models in eye image evaluation, indicating the potential medical interest of our method. Furthermore, our approach improves interpretability by explicitly aligning visual representations with structured clinical knowledge, enabling more structured and clinically grounded explanations than conventional models.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8836-8849"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11455158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning-Enhanced Dual-View GAT-Based Multi-Task Learning for Non-Coding RNA-Disease Association Prediction","authors":"Keichin Ng;Yulian Ding;Yanjie Wei;Yajun Liu;Yi Pan","doi":"10.1109/JBHI.2026.3678281","DOIUrl":"10.1109/JBHI.2026.3678281","url":null,"abstract":"Non-coding RNAs (ncRNAs), particularly long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are important regulators of gene expression and are closely involved in disease pathogenesis. Therefore, identifying ncRNA–disease associations is essential for clarifying disease mechanisms. Because lncRNAs and miRNAs frequently regulate each other in cells, predicting miRNA-disease associations (MDA) and lncRNA-disease associations (LDA) is inherently interconnected. However, many existing computational approaches still treat these tasks as independent problems, which limits their ability to capture cross-task biological signals. In addition, most models use fixed hyperparameters, which may be suboptimal as training dynamics and data characteristics change. To address these issues, we propose RL-DMGLMD (Reinforcement Learning-enhanced Dual-view Multi-task Graph learning for LncRNA-MiRNA-Disease association prediction). RL-DMGLMD contains three key components: (1) a Soft Actor-Critic (SAC) controller that adaptively tunes hyperparameters during training by monitoring loss, validation performance, and gradient information; (2) a unified multi-task framework that jointly predicts LDA, MDA, and lncRNA-miRNA interactions (LMI) using shared encoders with task-specific decoders to enable knowledge transfer; and (3) a dual-view multi-head Graph Attention Network (GAT) that learns from both heterogeneous interaction graphs and attribute graphs to capture relation-specific importance. RL-DMGLMD achieves <italic>AUROC</i> values of 0.9900 / 0.9872 / 0.9867 on Dataset 1 and 0.9946 / 0.9903 / 0.9954 on Dataset 2 for LDA, MDA, and LMI, respectively. These results outperform state-of-the-art baselines and support RL-DMGLMD as a practical tool for biomarker discovery and therapeutic target prioritization.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8597-8609"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11457322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147528518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Blockchain-Enabled AI-Driven Secure Searchable Encryption Framework for Medical IoT Systems","authors":"Salabat Khan;Mansoor Khan;Muhammad Asghar Khan;Muhammad Attique Khan;Lu Wang;Kaishun Wu","doi":"10.1109/JBHI.2025.3538623","DOIUrl":"10.1109/JBHI.2025.3538623","url":null,"abstract":"Blockchain technology is widely adopted in the Internet of Medical Things (IoMT) for information storage and retrieval. The integration of blockchain with IoMT systems enhances security; however, it raises privacy and security in data searching and storage. This study proposes a novel Binary Spring Search (BSS) technique based on group theory and integrated with a hybrid deep neural network approach to enhance the security and trustworthiness of IoMT. The proposed method incorporates secure key revocation and dynamic policy updates. The proposed framework leverages blockchain technology for immutable and decentralized data management, Artificial Intelligence (AI) for dynamic data analysis and threat detection, and advanced searchable encryption techniques to facilitate secure and efficient data queries. The proposed patient-centered data access model that combines blockchain technology with trust chains makes our method safer and more efficient and demonstrates a return on investment. Furthermore, our blockchain-based architecture ensures the integrity and immutability of medical data generated by IoMT devices, allowing for decentralized and tamper-proof storage. We used the hyper-ledger fabric tool, known as OrigionLab, for simulations in a blockchain context. We claim that the suggested framework provides a more searchable and secure solution to the healthcare system when compared to the other methods given through our findings. The simulation results show that our algorithm significantly reduces transaction time while maintaining high levels of security, making it a robust solution for managing Patient Health Records (PHR) in a decentralized manner.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8431-8444"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Awareness- and Arousal-Specific Brain Analysis Predicts Spinal Cord Stimulation Effectiveness in Disorders of Consciousness","authors":"Jiewei Lu;Qianqian Ge;Yinuo Liu;Jianghong He;Jianda Han;Ningbo Yu","doi":"10.1109/JBHI.2026.3677372","DOIUrl":"10.1109/JBHI.2026.3677372","url":null,"abstract":"Spinal cord stimulation (SCS) is an advanced treatment for disorders of consciousness (DoC), but its success rate varies between 30 and 60%, and consciousness-related biomarkers are urgently needed for SCS assessment and optimization. This paper proposes an awareness- and arousal-specific brain analysis (AAA) method to encode these two consciousness dimensions and construct consciousness-related features to predict SCS outcomes of DoC patients. Firstly, electroencephalogram (EEG) brain signals were collected from twenty-eight DoC patients during SCS treatment. Then, dynamic brain networks are formulated based on sliding-window correlation analysis of weighted phase lag index. Afterwards, a hierarchical network decomposition algorithm was developed to resolve the dynamic networks into consciousness-related networks by elaborating data-driven optimization of non-negative matrix factorization and consciousness-related variability. Further, consciousness features are designed to quantify the activation, interaction, and stability of awareness- and arousal-specific networks, and a support vector machine is trained for classification of SCS treatment effectiveness. Clinical results showed that our method achieved an overall prediction accuracy of 88%, which is significantly better than clinical accuracy of 50% by doctors. Moreover, our method outperformed existing EEG-based approaches in both prediction accuracy and pathology interpretability. The proposed awareness- and arousal-specific brain analysis method establishes a pivotal framework for precise and reliable SCS treatment of DoC.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8506-8518"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Conversational Literature Retrieval Quality via Personalized Profile-Based Re-Ranking","authors":"ShuaiYu Zhang;Huihui Shao;Zhenping Xie","doi":"10.1109/JBHI.2026.3669741","DOIUrl":"10.1109/JBHI.2026.3669741","url":null,"abstract":"Academic literature retrieval is constrained by the paradox of “information overload” versus “evidence scarcity”, a tension that deepens when researchers iteratively refine their queries in multiturn conversational settings. To address this challenge, we propose Conversational Literature Personalized Reranking (CLPR), a personalized framework that unifies dense semantic retrieval with personalized user profiling. CLPR first performs a broad high-recall retrieval to collect candidate documents, then compresses conversational history into a concise textual profile that encodes sequential continuity, immediate focus, and longterm research background via a large language model. The generated profile serves as a pseudo-query for a neural crossencoder to produce the final ranking. Cross-domain testing on the public LitSearch (computer science) benchmark confirms its robust generalization, yielding an NDCG@10 of 0.4793. On MedCorpus, a new multi-turn biomedical conversational retrieval benchmark constructed for this study, CLPR attains state-of-the-art performance with P@1 = 0.9497 and NDCG@10 = 0.9271, surpassing the strongest baseline by substantial margins. Ablation shows longterm background cues contribute most, and maintaining a short, uptodate profile across turns outperforms a static one. CLPR therefore delivers accurate, personalized literature retrieval and can accelerate evidence synthesis across scientific domains.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8380-8391"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147348382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}